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John Einmahl

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Einmahl, Jesson & Einmahl, John & de Haan, L.F.M., 2017. "Limits to Human Life Span Through Extreme Value Theory," Discussion Paper 2017-051, Tilburg University, Center for Economic Research.

    Mentioned in:

    1. Sam Watson’s journal round-up for 3rd June 2019
      by Sam Watson in The Academic Health Economists' Blog on 2019-06-03 11:00:40

Working papers

  1. Einmahl, John & He, Y., 2020. "Unified Extreme Value Estimation for Heterogeneous Data," Discussion Paper 2020-025, Tilburg University, Center for Economic Research.

    Cited by:

    1. Milian Bachem & Lerby Ergun & Casper de Vries, 2021. "Covariates Hiding in the Tails," Staff Working Papers 21-45, Bank of Canada.

  2. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.

    Cited by:

    1. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.
    2. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Discussion Paper 2023-001, Tilburg University, Center for Economic Research.

  3. Einmahl, Jesson & Einmahl, John & de Haan, L.F.M., 2017. "Limits to Human Life Span Through Extreme Value Theory," Discussion Paper 2017-051, Tilburg University, Center for Economic Research.

    Cited by:

    1. Huang, Fei & Maller, Ross & Ning, Xu, 2020. "Modelling life tables with advanced ages: An extreme value theory approach," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 95-115.
    2. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    3. Boulfani, Fériel & Gendre, Xavier & Ruiz-Gazen, Anne & Salvignol, Martina, 2021. "A statistical approach for sizing an aircraft electrical generator using extreme value theory," TSE Working Papers 21-1274, Toulouse School of Economics (TSE).
    4. Broeders, Dirk & Mehlkopf, Roel & van Ool, Annick, 2021. "The economics of sharing macro-longevity risk," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 440-458.
    5. Camarda, Carlo Giovanni, 2022. "The curse of the plateau. Measuring confidence in human mortality estimates at extreme ages," Theoretical Population Biology, Elsevier, vol. 144(C), pages 24-36.

  4. Beirlant, J. & Kijko, Andrzej & Reykens, Tom & Einmahl, John, 2017. "Estimating the Maximum Possible Earthquake Magnitude Using Extreme Value Methodology : the Groningen Case," Discussion Paper 2017-050, Tilburg University, Center for Economic Research.

    Cited by:

    1. Nurulkamal Masseran & Muhammad Aslam Mohd Safari, 2021. "Mixed POT-BM Approach for Modeling Unhealthy Air Pollution Events," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    2. Minakshi Mishra & Abhishek & R. B. S. Yadav & Manisha Sandhu, 2021. "Probabilistic assessment of earthquake hazard in the Andaman–Nicobar–Sumatra region," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 313-338, January.

  5. Einmahl, John & Kiriliouk, Anna & Segers, Johan, 2016. "A continuous updating weighted least squares estimator of tail dependence in high dimensions," LIDAM Discussion Papers ISBA 2016002, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Miranda J. Fix & Daniel S. Cooley & Emeric Thibaud, 2021. "Simultaneous autoregressive models for spatial extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    2. Samuel A. Morris & Brian J. Reich & Emeric Thibaud, 2019. "Exploration and Inference in Spatial Extremes Using Empirical Basis Functions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 555-572, December.
    3. Kiriliouk, Anna & Lee, Jeongjin & Segers, Johan, 2023. "X-Vine Models for Multivariate Extremes," LIDAM Discussion Papers ISBA 2023038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Gissibl, Nadine & Klüppelberg, Claudia & Otto, Moritz, 2018. "Tail dependence of recursive max-linear models with regularly varying noise variables," Econometrics and Statistics, Elsevier, vol. 6(C), pages 149-167.
    6. Einmahl, John & Segers, Johan, 2020. "Empirical Tail Copulas for Functional Data," Discussion Paper 2020-004, Tilburg University, Center for Economic Research.
    7. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    8. Klüppelberg, Claudia & Krali, Mario, 2021. "Estimating an extreme Bayesian network via scalings," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    9. Segers, Johan, 2019. "One- versus multi-component regular variation and extremes of Markov trees," LIDAM Discussion Papers ISBA 2019001, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Hentschel, Manuel & Engelke, Sebastian & Segers, Johan, 2022. "Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions," LIDAM Discussion Papers ISBA 2022032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Asenova, Stefka Kirilova & Mazo, Gildas & Segers, Johan, 2020. "Inference on extremal dependence in a latent Markov tree model attracted to a Husler-Reiss distribution," LIDAM Discussion Papers ISBA 2020005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    12. Nadine Gissibl & Claudia Klüppelberg & Steffen Lauritzen, 2021. "Identifiability and estimation of recursive max‐linear models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 188-211, March.

  6. Einmahl, J.H.J. & Li, Jun & Liu, Regina, 2015. "Bridging Centrality and Extremity : Refining Empirical Data Depth using Extreme Value Statistics," Discussion Paper 2015-020, Tilburg University, Center for Economic Research.

    Cited by:

    1. He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Other publications TiSEM d6529c8a-8865-4c03-a064-a, Tilburg University, School of Economics and Management.
    2. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.
    3. Stanislav Nagy, 2021. "Halfspace depth does not characterize probability distributions," Statistical Papers, Springer, vol. 62(3), pages 1135-1139, June.

  7. Einmahl, John & Kiriliouk, Anna & Krajina, Andrea & Segers, Johan, 2014. "An M-estimator of spatial tail dependence," LIDAM Discussion Papers ISBA 2014008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Einmahl, John & Kiriliouk, A. & Segers, J.J.J., 2016. "A Continuous Updating Weighted Least Squares Estimator of Tail Dependence in High Dimensions," Discussion Paper 2016-002, Tilburg University, Center for Economic Research.
    2. Miranda J. Fix & Daniel S. Cooley & Emeric Thibaud, 2021. "Simultaneous autoregressive models for spatial extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 32(2), March.
    3. R de Fondeville & A C Davison, 2018. "High-dimensional peaks-over-threshold inference," Biometrika, Biometrika Trust, vol. 105(3), pages 575-592.
    4. Rootzen, Holger & Segers, Johan & Wadsworth, Jenny, 2016. "Multivariate peaks over thresholds models," LIDAM Discussion Papers ISBA 2016018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    7. Klüppelberg, Claudia & Krali, Mario, 2021. "Estimating an extreme Bayesian network via scalings," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    8. Hentschel, Manuel & Engelke, Sebastian & Segers, Johan, 2022. "Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions," LIDAM Discussion Papers ISBA 2022032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Robert, Christian Y., 2022. "Testing for changes in the tail behavior of Brown–Resnick Pareto processes," Stochastic Processes and their Applications, Elsevier, vol. 144(C), pages 312-368.
    10. Kiriliouk, Anna & Segers, Johan & Warchol, Michal, 2014. "Nonparametric estimation of extremal dependence," LIDAM Discussion Papers ISBA 2014044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    12. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks," Papers 2304.06950, arXiv.org.
    13. Kiriliouk, Anna, 2017. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space with application to generalized max-linear models," LIDAM Discussion Papers ISBA 2017027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  8. Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2014. "Statistics of Heteroscedastic Extremes," Discussion Paper 2014-015, Tilburg University, Center for Economic Research.

    Cited by:

    1. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
    2. Jacek Wójcik, 2017. "Consequences of the Cognitive Digital Divide on the Consumer Market," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 44, pages 69-80.
    3. Einmahl, John & Ferreira, Ana & de Haan, Laurens & Neves, C. & Zhou, C., 2020. "Spatial Dependence and Space-Time Trend in Extreme Events," Discussion Paper 2020-009, Tilburg University, Center for Economic Research.
    4. Wittenberg, Raphael & Sharpin, Luke & McCormick, Barry & Hurst, Jeremy, 2017. "The ageing society and emergency hospital admissions," Health Policy, Elsevier, vol. 121(8), pages 923-928.
    5. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Discussion Paper 2022-017, Tilburg University, Center for Economic Research.
    6. Chen, Yu & Ma, Mengyuan & Sun, Hongfang, 2023. "Statistical inference for extreme extremile in heavy-tailed heteroscedastic regression model," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 142-162.
    7. Powell, Robert J. & Vo, Duc H. & Pham, Thach N. & Singh, Abhay K., 2017. "The long and short of commodity tails and their relationship to Asian equity markets," Journal of Asian Economics, Elsevier, vol. 52(C), pages 32-44.
    8. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Other publications TiSEM fd8dd91c-086f-40e6-ac29-3, Tilburg University, School of Economics and Management.
    9. Alois Guger, 1996. "Redistribution by the State in Austria," Austrian Economic Quarterly, WIFO, vol. 1(4), pages 185-196, October.
    10. Dominković, D.F. & Bačeković, I. & Ćosić, B. & Krajačić, G. & Pukšec, T. & Duić, N. & Markovska, N., 2016. "Zero carbon energy system of South East Europe in 2050," Applied Energy, Elsevier, vol. 184(C), pages 1517-1528.
    11. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    12. Pinchasik, Daniel Ruben & Hovi, Inger Beate, 2017. "A CO2-fund for the transport industry: The case of Norway," Transport Policy, Elsevier, vol. 53(C), pages 186-195.
    13. Magdeldin, Mohamed & Kohl, Thomas & Järvinen, Mika, 2017. "Techno-economic assessment of the by-products contribution from non-catalytic hydrothermal liquefaction of lignocellulose residues," Energy, Elsevier, vol. 137(C), pages 679-695.
    14. Fuchs, Johann & Hummel, Markus & Hutter, Christian & Gehrke, Britta & Wanger, Susanne & Weber, Enzo & Weigand, Roland & Zika, Gerd, 2016. "IAB-Prognose 2016: Beschäftigung und Arbeitskräfteangebot so hoch wie nie (IAB forecast 2016: Employment and labour supply are higher than ever before)," IAB-Kurzbericht 201606, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    15. Paulo M.M. Rodrigues & João Nicolau, 2023. "Tail index estimation in the presence of covariates: Stock returns’ tail risk dynamics," Working Papers w202306, Banco de Portugal, Economics and Research Department.
    16. Einmahl, John & He, Y., 2020. "Unified Extreme Value Estimation for Heterogeneous Data," Discussion Paper 2020-025, Tilburg University, Center for Economic Research.
    17. Joos, Michael & Staffell, Iain, 2018. "Short-term integration costs of variable renewable energy: Wind curtailment and balancing in Britain and Germany," Renewable and Sustainable Energy Reviews, Elsevier, vol. 86(C), pages 45-65.
    18. Nicoletti, Giuseppe & von Rueden, Christina & Andrews, Dan, 2020. "Digital technology diffusion: A matter of capabilities, incentives or both?," European Economic Review, Elsevier, vol. 128(C).
    19. Einmahl, John & He, Y., 2020. "Unified Extreme Value Estimation for Heterogeneous Data," Other publications TiSEM dfe6c38c-823b-4394-b4fd-a, Tilburg University, School of Economics and Management.
    20. Yaolan Ma & Bo Wei & Wei Huang, 2020. "A nonparametric estimator for the conditional tail index of Pareto-type distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(1), pages 17-44, January.
    21. Li, Wei & Lu, Can & Ding, Yi & Zhang, Yan-Wu, 2017. "The impacts of policy mix for resolving overcapacity in heavy chemical industry and operating national carbon emission trading market in China," Applied Energy, Elsevier, vol. 204(C), pages 509-524.
    22. Brook T. Russell & Whitney K. Huang, 2021. "Modeling short‐ranged dependence in block extrema with application to polar temperature data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    23. Andrea Kunnert, 2016. "Leistbarkeit von Wohnen in Österreich. Operationalisierung und demographische Komponenten," WIFO Studies, WIFO, number 58932, April.
    24. Kang, Sang Hoon & Islam, Faridul & Kumar Tiwari, Aviral, 2019. "The dynamic relationships among CO2 emissions, renewable and non-renewable energy sources, and economic growth in India: Evidence from time-varying Bayesian VAR model," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 90-101.
    25. Ljungberg, Anders, 2016. "Marginal cost-pricing in the Swedish transport sector – An efficient and sustainable way of funding local and regional public transport in the future?," Research in Transportation Economics, Elsevier, vol. 59(C), pages 159-166.
    26. Einmahl, John & Zhou, C., 2024. "Tail Copula Estimation for Heteroscedastic Extremes," Other publications TiSEM 6bcb09c5-8b19-48b8-9320-b, Tilburg University, School of Economics and Management.
    27. Demichelis, Francesca & Fiore, Silvia & Pleissner, Daniel & Venus, Joachim, 2018. "Technical and economic assessment of food waste valorization through a biorefinery chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 38-48.
    28. Robert, Christian Y., 2022. "Testing for changes in the tail behavior of Brown–Resnick Pareto processes," Stochastic Processes and their Applications, Elsevier, vol. 144(C), pages 312-368.
    29. Einmahl, John & Zhou, C., 2024. "Tail Copula Estimation for Heteroscedastic Extremes," Discussion Paper 2024-003, Tilburg University, Center for Economic Research.
    30. Cui, Hengxin & Tan, Ken Seng & Yang, Fan & Zhou, Chen, 2022. "Asymptotic analysis of portfolio diversification," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 302-325.
    31. Schweiger, Gerald & Rantzer, Jonatan & Ericsson, Karin & Lauenburg, Patrick, 2017. "The potential of power-to-heat in Swedish district heating systems," Energy, Elsevier, vol. 137(C), pages 661-669.
    32. Borozan, Djula, 2018. "Regional-level household energy consumption determinants: The european perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 347-355.
    33. Lu, Ying & Prato, Carlo G. & Sipe, Neil & Kimpton, Anthony & Corcoran, Jonathan, 2022. "The role of household modality style in first and last mile travel mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 95-109.
    34. Everard, Mark & Longhurst, James & Pontin, John & Stephenson, Wendy & Brooks, Joss, 2017. "Developed-developing world partnerships for sustainable development (2): An illustrative case for a payments for ecosystem services (PES) approach," Ecosystem Services, Elsevier, vol. 24(C), pages 253-260.

  9. He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Discussion Paper 2014-035, Tilburg University, Center for Economic Research.

    Cited by:

    1. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
    2. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.
    3. Ebers Broughel, Anna & Hampl, Nina, 2018. "Community financing of renewable energy projects in Austria and Switzerland: Profiles of potential investors," Energy Policy, Elsevier, vol. 123(C), pages 722-736.
    4. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Discussion Paper 2023-001, Tilburg University, Center for Economic Research.
    5. Chen, Simiao & Prettner, Klaus & Kuhn, Michael & Bloom, David E., 2021. "The economic burden of COVID-19 in the United States: Estimates and projections under an infection-based herd immunity approach," The Journal of the Economics of Ageing, Elsevier, vol. 20(C).
    6. Feng, Xiang-Nan & Wang, Yifan & Lu, Bin & Song, Xin-Yuan, 2017. "Bayesian regularized quantile structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 234-248.
    7. Singh, Ripudaman & Kemausuor, Francis & Wooldridge, Margaret, 2018. "Locational analysis of cellulosic ethanol production and distribution infrastructure for the transportation sector in Ghana," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 393-406.
    8. Felten, Björn & Weber, Christoph, 2018. "The value(s) of flexible heat pumps – Assessment of technical and economic conditions," Applied Energy, Elsevier, vol. 228(C), pages 1292-1319.

  10. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2012. "Estimation of the Marginal Expected Shortfall : The Mean when a Related Variable is Extreme," Discussion Paper 2012-080, Tilburg University, Center for Economic Research.

    Cited by:

    1. Chen, Yu & Gao, Yu & Shu, Lei & Zhu, Xiaonan, 2023. "Network effects on risk co-movements: A network quantile autoregression-based analysis," Finance Research Letters, Elsevier, vol. 56(C).
    2. Gribkova, N.V. & Su, J. & Zitikis, R., 2022. "Inference for the tail conditional allocation: Large sample properties, insurance risk assessment, and compound sums of concomitants," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 199-222.
    3. Andrea Teruzzi, 2023. "Tail Risk and Systemic Risk Estimation of Cryptocurrencies: an Expectiles and Marginal Expected Shortfall based approach," Papers 2311.17239, arXiv.org.
    4. Qin, Xiao & Zhou, Chunyang, 2019. "Financial structure and determinants of systemic risk contribution," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    5. Das Bikramjit & Fasen-Hartmann Vicky, 2019. "Conditional excess risk measures and multivariate regular variation," Statistics & Risk Modeling, De Gruyter, vol. 36(1-4), pages 1-23, December.
    6. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2017. "Extreme M-quantiles as risk measures: From L1 to Lp optimization," TSE Working Papers 17-841, Toulouse School of Economics (TSE).
    7. Mao, Tiantian & Stupfler, Gilles & Yang, Fan, 2023. "Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 173-192.
    8. Sun, Hongfang & Chen, Yu & Hu, Taizhong, 2022. "Statistical inference for tail-based cumulative residual entropy," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 66-95.
    9. Popescu, Alexandra & Turcu, Camelia, 2017. "Sovereign debt and systemic risk in the eurozone," Economic Modelling, Elsevier, vol. 67(C), pages 275-284.
    10. S. Tavolaro & F. Visnovsky, 2014. "What is the information content of the SRISK measure as a supervisory tool?," Débats économiques et financiers 10, Banque de France.
    11. Ling, Chengxiu, 2019. "Asymptotics of multivariate conditional risk measures for Gaussian risks," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 205-215.
    12. Hua, Lei & Joe, Harry, 2014. "Strength of tail dependence based on conditional tail expectation," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 143-159.
    13. Rodrigo S. Targino & Gareth W. Peters & Pavel V. Shevchenko, 2014. "Sequential Monte Carlo Samplers for capital allocation under copula-dependent risk models," Papers 1410.1101, arXiv.org, revised Feb 2015.
    14. Beck, Nicholas & Di Bernardino, Elena & Mailhot, Mélina, 2021. "Semi-parametric estimation of multivariate extreme expectiles," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    15. Asimit, Alexandru V. & Li, Jinzhu, 2016. "Extremes for coherent risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 332-341.
    16. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2018. "Tail expectile process and risk assessment," TSE Working Papers 18-944, Toulouse School of Economics (TSE).
    17. Li, Jinzhu, 2022. "Asymptotic results on marginal expected shortfalls for dependent risks," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 146-168.
    18. Bousebata, Meryem & Enjolras, Geoffroy & Girard, Stéphane, 2023. "Extreme partial least-squares," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    19. Goegebeur, Yuri & Guillou, Armelle & Ho, Nguyen Khanh Le & Qin, Jing, 2023. "A Weissman-type estimator of the conditional marginal expected shortfall," Econometrics and Statistics, Elsevier, vol. 27(C), pages 173-196.
    20. Cristina Zeldea, 2020. "Modeling the Connection between Bank Systemic Risk and Balance-Sheet Liquidity Proxies through Random Forest Regressions," Administrative Sciences, MDPI, vol. 10(3), pages 1-14, August.
    21. N. V. Gribkova & J. Su & R. Zitikis, 2022. "Empirical tail conditional allocation and its consistency under minimal assumptions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 713-735, August.
    22. Liu, Ruicheng & Pun, Chi Seng, 2022. "Machine-Learning-enhanced systemic risk measure: A Two-Step supervised learning approach," Journal of Banking & Finance, Elsevier, vol. 136(C).
    23. Beatriz de la Flor & Javier Ojea-Ferreiro & Eva Ferreira, 2022. "The Hedging Cost of Forgetting the Exchange Rate," Documentos de Trabajo del ICAE 2022-01, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    24. Takashi Isogai, 2014. "Benchmarking of Unconditional VaR and ES Calculation Methods: A Comparative Simulation Analysis with Truncated Stable Distribution," Bank of Japan Working Paper Series 14-E-1, Bank of Japan.
    25. Areski Cousin & Elena Di Bernardino, 2013. "On Multivariate Extensions of Conditional-Tail-Expectation," Working Papers hal-00877386, HAL.
    26. Das, Bikramjit & Fasen-Hartmann, Vicky, 2018. "Risk contagion under regular variation and asymptotic tail independence," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 194-215.
    27. Bikramjit Das & Vicky Fasen, 2016. "Risk contagion under regular variation and asymptotic tail independence," Papers 1603.09406, arXiv.org, revised Apr 2017.
    28. Goegebeur, Yuri & Guillou, Armelle & Ho, Nguyen Khanh Le & Qin, Jing, 2023. "Nonparametric estimation of conditional marginal excess moments," Journal of Multivariate Analysis, Elsevier, vol. 193(C).
    29. Landsman, Zinoviy & Makov, Udi & Shushi, Tomer, 2016. "Multivariate tail conditional expectation for elliptical distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 216-223.
    30. Laurent Gardes & Stéphane Girard & Gilles Stupfler, 2020. "Beyond tail median and conditional tail expectation: Extreme risk estimation using tail Lp‐optimization," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 922-949, September.
    31. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2021. "ExpectHill estimation, extreme risk and heavy tails," Journal of Econometrics, Elsevier, vol. 221(1), pages 97-117.
    32. Ji, Liuyan & Tan, Ken Seng & Yang, Fan, 2021. "Tail dependence and heavy tailedness in extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 282-293.
    33. Qin, Xiao & Zhou, Chen, 2021. "Systemic risk allocation using the asymptotic marginal expected shortfall," Journal of Banking & Finance, Elsevier, vol. 126(C).
    34. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
    35. Hou, Yanxi & Wang, Xing, 2019. "Nonparametric inference for distortion risk measures on tail regions," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 92-110.

  11. Einmahl, J.H.J. & Magnus, J.R. & Kumar, K., 2011. "On the Choice of Prior in Bayesian Model Averaging," Discussion Paper 2011-003, Tilburg University, Center for Economic Research.

    Cited by:

    1. Mattia Filomena & Matteo Picchio, 2023. "Retirement and health outcomes in a meta‐analytical framework," Journal of Economic Surveys, Wiley Blackwell, vol. 37(4), pages 1120-1155, September.
    2. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2011. "A Generalized Missing-Indicator Approach to Regression with Imputed Covariates," EIEF Working Papers Series 1111, Einaudi Institute for Economics and Finance (EIEF), revised May 2011.
    3. António Afonso & João Tovar Jalles, 2017. "Quantitative Easing and Sovereign Yield Spreads: Euro-Area Time-Varying Evidence," Working Papers REM 2017/20, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    4. António Afonso & Florence Huart & João Tovar Jalles & Piotr Stanek, 2018. "Twin Deficits Revisited: a role for fiscal institutions?," Working Papers REM 2018/31, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    5. De Luca, G. & Magnus, J.R., 2011. "Bayesian Model Averaging and Weighted Average Least Squares : Equivariance, Stability, and Numerical Issues," Discussion Paper 2011-082, Tilburg University, Center for Economic Research.
    6. Quang T. T. Nguyen & Son T. B. Nguyen & Quang V. Nguyen, 2019. "Can Higher Capital Discipline Bank Risk: Evidence from a Meta-Analysis," JRFM, MDPI, vol. 12(3), pages 1-21, August.
    7. Judith Anne Clarke, 2017. "Model Averaging OLS and 2SLS: An Application of the WALS Procedure," Econometrics Working Papers 1701, Department of Economics, University of Victoria.

  12. EINMAHL, John H.J. & KRAJINA, Andrea & Segers, Johan, 2011. "An M-Estimator For Tail Dependence In Arbitrary Dimensions," LIDAM Discussion Papers ISBA 2011005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

    Cited by:

    1. Einmahl, John & Kiriliouk, A. & Segers, J.J.J., 2016. "A Continuous Updating Weighted Least Squares Estimator of Tail Dependence in High Dimensions," Discussion Paper 2016-002, Tilburg University, Center for Economic Research.
    2. Goix, Nicolas & Sabourin, Anne & Clémençon, Stephan, 2017. "Sparse representation of multivariate extremes with applications to anomaly detection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 12-31.
    3. Chollete, Lorán & de la Peña, Victor & Lu, Ching-Chih, 2012. "International diversification: An extreme value approach," Journal of Banking & Finance, Elsevier, vol. 36(3), pages 871-885.
    4. Helena Ferreira & Marta Ferreira, 2021. "Tail dependence and smoothness of time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 198-210, March.
    5. Fougères, Anne-Laure & Mercadier, Cécile & Nolan, John P., 2013. "Dense classes of multivariate extreme value distributions," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 109-129.
    6. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "Beyond dimension two: A test for higher-order tail risk," SFB 649 Discussion Papers SFB649DP2014-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Xin Lao & Zuoxiang Peng & Saralees Nadarajah, 2023. "Tail Dependence Functions of Two Classes of Bivariate Skew Distributions," Methodology and Computing in Applied Probability, Springer, vol. 25(1), pages 1-24, March.
    8. John H. J. Einmahl & Anna Kiriliouk & Andrea Krajina & Johan Segers, 2016. "An M-estimator of spatial tail dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 275-298, January.
    9. Mourahib, Anas & Kiriliouk, Anna & Segers, Johan, 2023. "Multivariate generalized Pareto distributions along extreme directions," LIDAM Discussion Papers ISBA 2023034, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Kiriliouk, Anna & Segers, Johan & Tafakori, Laleh, 2018. "An estimator of the stable tail dependence function based on the empirical beta copula," LIDAM Discussion Papers ISBA 2018029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Marta Ferreira & Helena Ferreira, 2013. "Extremes of multivariate ARMAX processes," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 606-627, November.
    12. Rootzen, Holger & Segers, Johan & Wadsworth, Jenny, 2016. "Multivariate peaks over thresholds models," LIDAM Discussion Papers ISBA 2016018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    13. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Bücher, Axel & Volgushev, Stanislav & Zou, Nan, 2019. "On second order conditions in the multivariate block maxima and peak over threshold method," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 604-619.
    15. Bücher Axel, 2014. "A note on nonparametric estimation of bivariate tail dependence," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 1-12, June.
    16. Rootzen, Holger & Segers, Johan & Wadsworth, Jennifer, 2017. "Multivariate generalized Pareto distributions: parametrizations, representations, and properties," LIDAM Discussion Papers ISBA 2017016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. Asenova, Stefka & Segers, Johan, 2022. "Max-linear graphical models with heavy-tailed factors on trees of transitive tournaments," LIDAM Discussion Papers ISBA 2022031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Bormann, Carsten & Schienle, Melanie, 2019. "Detecting structural differences in tail dependence of financial time series," Working Paper Series in Economics 122, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    19. Gissibl, Nadine & Klüppelberg, Claudia & Otto, Moritz, 2018. "Tail dependence of recursive max-linear models with regularly varying noise variables," Econometrics and Statistics, Elsevier, vol. 6(C), pages 149-167.
    20. Falk, Michael & Padoan, Simone A. & Wisheckel, Florian, 2019. "Generalized Pareto copulas: A key to multivariate extremes," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    21. Einmahl, John & Segers, Johan, 2020. "Empirical Tail Copulas for Functional Data," Discussion Paper 2020-004, Tilburg University, Center for Economic Research.
    22. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    23. Kiriliouk, Anna & Segers, Johan & Tafakori, Laleh, 2017. "An estimator of the stable tail dependence function based on the empirical beta copula," LIDAM Discussion Papers ISBA 2017028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    24. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    25. Klüppelberg, Claudia & Krali, Mario, 2021. "Estimating an extreme Bayesian network via scalings," Journal of Multivariate Analysis, Elsevier, vol. 181(C).
    26. Einmahl, John & Zhou, C., 2024. "Tail Copula Estimation for Heteroscedastic Extremes," Other publications TiSEM 6bcb09c5-8b19-48b8-9320-b, Tilburg University, School of Economics and Management.
    27. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "A Test for the Portion of Bivariate Dependence in Multivariate Tail Risk," Tinbergen Institute Discussion Papers 14-024/III, Tinbergen Institute, revised 23 Jun 2014.
    28. Segers, Johan, 2012. "Max-Stable Models For Multivariate Extremes," LIDAM Discussion Papers ISBA 2012011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    29. Hentschel, Manuel & Engelke, Sebastian & Segers, Johan, 2022. "Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions," LIDAM Discussion Papers ISBA 2022032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    30. Kiriliouk, Anna & Segers, Johan & Warchol, Michal, 2014. "Nonparametric estimation of extremal dependence," LIDAM Discussion Papers ISBA 2014044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    31. Einmahl, John & Zhou, C., 2024. "Tail Copula Estimation for Heteroscedastic Extremes," Discussion Paper 2024-003, Tilburg University, Center for Economic Research.
    32. Rootzén, Holger & Segers, Johan & Wadsworth, Jennifer L., 2018. "Multivariate generalized Pareto distributions: Parametrizations, representations, and properties," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 117-131.
    33. Asenova, Stefka Kirilova & Mazo, Gildas & Segers, Johan, 2020. "Inference on extremal dependence in a latent Markov tree model attracted to a Husler-Reiss distribution," LIDAM Discussion Papers ISBA 2020005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    34. Kiriliouk, Anna, 2017. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space with application to generalized max-linear models," LIDAM Discussion Papers ISBA 2017027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    35. Chiapino, Mael & Sabourin, Anne & Segers, Johan, 2018. "Identifying groups of variables with the potential of being large simultaneously," LIDAM Discussion Papers ISBA 2018006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    36. Bucher, Axel & Jaschke, Stefan & Wied, Dominik, 2013. "Nonparametric tests for constant tail dependence with an application to energy and finance," LIDAM Discussion Papers ISBA 2013033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  13. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M., 2011. "Estimation of extreme risk regions under multivariate regular variation," Other publications TiSEM b7a72a8d-f9bc-4129-ae9b-a, Tilburg University, School of Economics and Management.

    Cited by:

    1. He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Other publications TiSEM d6529c8a-8865-4c03-a064-a, Tilburg University, School of Economics and Management.
    2. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
    3. Berthet, Philippe & Einmahl, John, 2020. "Cube Root Weak Convergence of Empirical Estimators of a Density Level Set," Discussion Paper 2020-015, Tilburg University, Center for Economic Research.
    4. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.
    5. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
    6. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Discussion Paper 2023-001, Tilburg University, Center for Economic Research.
    7. Einmahl, J.H.J. & Li, Jun & Liu, Regina, 2015. "Bridging Centrality and Extremity : Refining Empirical Data Depth using Extreme Value Statistics," Other publications TiSEM bcd9783a-e07e-4da2-bc47-b, Tilburg University, School of Economics and Management.
    8. Yves Dominicy & Pauliina Ilmonen & David Veredas, 2017. "Multivariate Hill Estimators," International Statistical Review, International Statistical Institute, vol. 85(1), pages 108-142, April.

  14. Einmahl, J.H.J. & Gantner, M., 2010. "Testing for Bivariate Spherical Symmetry," Discussion Paper 2010-71, Tilburg University, Center for Economic Research.

    Cited by:

    1. Francq, C. & Jiménez-Gamero, M.D. & Meintanis, S.G., 2017. "Tests for conditional ellipticity in multivariate GARCH models," Journal of Econometrics, Elsevier, vol. 196(2), pages 305-319.

  15. Einmahl, J.H.J. & Smeets, S.G.W.R., 2009. "Ultimate 100m World Records Through Extreme-Value Theory," Discussion Paper 2009-57, Tilburg University, Center for Economic Research.

    Cited by:

    1. Christina Empacher & Udo Kamps & Grigoriy Volovskiy, 2023. "Statistical Prediction of Future Sports Records Based on Record Values," Stats, MDPI, vol. 6(1), pages 1-17, January.
    2. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.
    3. Gbari, Kock Yed Ake Samuel & Poulain, Michel & Dal, Luc & Denuit, Michel, 2016. "Extreme value analysis of mortality at the oldest ages: a case study based on individual ages at death," LIDAM Discussion Papers ISBA 2016012, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  16. Einmahl, J.H.J. & de Haan, L.F.M. & Krajina, A., 2009. "Estimating Extreme Bivariate Quantile Regions," Discussion Paper 2009-29, Tilburg University, Center for Economic Research.

    Cited by:

    1. He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Other publications TiSEM d6529c8a-8865-4c03-a064-a, Tilburg University, School of Economics and Management.
    2. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    3. Grothe, Oliver & Korniichuk, Volodymyr & Manner, Hans, 2014. "Modeling multivariate extreme events using self-exciting point processes," Journal of Econometrics, Elsevier, vol. 182(2), pages 269-289.

  17. Einmahl, J.H.J. & van Keilegom, I., 2008. "Specification tests in nonparametric regression," Other publications TiSEM 2c94c2d8-8305-4fb1-b47f-7, Tilburg University, School of Economics and Management.

    Cited by:

    1. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    2. Babii, Andrii & Florens, Jean-Pierre, 2017. "Are unobservables separable?," TSE Working Papers 17-802, Toulouse School of Economics (TSE).
    3. Hušková, Marie & Meintanis, Simos G. & Pretorius, Charl, 2020. "Tests for validity of the semiparametric heteroskedastic transformation model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    4. Florens, Jean-Pierre & Simar, Leopold & Van Keilegom, Ingrid, 2013. "Frontier estimation in nonparametric location-scale models," LIDAM Reprints ISBA 2013035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Racine, Jeffrey S. & Li, Kevin, 2017. "Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach," Journal of Econometrics, Elsevier, vol. 201(1), pages 72-94.
    6. Y. Andriyana & I. Gijbels & A. Verhasselt, 2018. "Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity," Statistical Papers, Springer, vol. 59(4), pages 1589-1621, December.
    7. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    8. Marek Omelka & Šárka Hudecová & Natalie Neumeyer, 2021. "Maximum pseudo‐likelihood estimation based on estimated residuals in copula semiparametric models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1433-1473, December.
    9. Fanjul-Hevia, Arís & González-Manteiga, Wenceslao & Pardo-Fernández, Juan Carlos, 2021. "A non-parametric test for comparing conditional ROC curves," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    10. Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
    11. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    12. Neumeyer, Natalie & Van Keilegom, Ingrid, 2010. "Estimating the error distribution in nonparametric multiple regression with applications to model testing," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1067-1078, May.
    13. Henderson Daniel J. & Parmeter Christopher F., 2017. "Root-n Consistent Kernel Density Estimation in Practice," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-10, January.
    14. Neumeyer, Natalie & Noh, Hohsuk & Van Keilegom, Ingrid, 2014. "Heteroscedastic semiparametric transformation models: estimation and testing for validity," LIDAM Discussion Papers ISBA 2014047, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Mastromarco, Camilla & Simar, Léopold & Van Keilegom, Ingrid, 2022. "Estimating Nonparametric Conditional Frontiers and Efficiencies: A New Approach," LIDAM Discussion Papers ISBA 2022035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    16. Van Keilegom, Ingrid, 2013. "Discussion on: "An updated review of Goodness-of-Fit tests for regression models" (by W. Gonzales-Manteiga and R.M. Crujeiras)," LIDAM Discussion Papers ISBA 2013008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    17. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2016. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Reprints ISBA 2016007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Sankar, Subhra & Bergsma, Wicher & Dassios, Angelos, 2017. "Testing independence of covariates and errors in nonparametric regression," LSE Research Online Documents on Economics 83780, London School of Economics and Political Science, LSE Library.
    19. Hlávka, Zdenek & Husková, Marie & Meintanis, Simos G., 2011. "Tests for independence in non-parametric heteroscedastic regression models," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 816-827, April.
    20. Feve, Frederique & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2012. "Estimation of conditional ranks and tests of exogeneity in nonparametric nonseparable models," LIDAM Discussion Papers ISBA 2012036, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    21. Teran Hidalgo, Sebastian J. & Wu, Michael C. & Engel, Stephanie M. & Kosorok, Michael R., 2018. "Goodness-of-fit test for nonparametric regression models: Smoothing spline ANOVA models as example," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 135-155.
    22. Elia Lapenta, 2022. "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors," Papers 2212.11112, arXiv.org, revised Oct 2023.

  18. Einmahl, J.H.J. & Segers, J.J.J., 2008. "Maximum Empirical Likelihood Estimation of the Spectral Measure of an Extreme Value Distribution," Discussion Paper 2008-42, Tilburg University, Center for Economic Research.

    Cited by:

    1. Padoan, Simone A., 2011. "Multivariate extreme models based on underlying skew-t and skew-normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 977-991, May.
    2. Goix, Nicolas & Sabourin, Anne & Clémençon, Stephan, 2017. "Sparse representation of multivariate extremes with applications to anomaly detection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 12-31.
    3. Holger Drees, 2012. "Extreme value analysis of actuarial risks: estimation and model validation," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 225-264, June.
    4. Gudendorf, Gordon & Segers, Johan, 2011. "Nonparametric estimation of an extreme-value copula in arbitrary dimensions," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 37-47, January.
    5. Sabourin, Anne, 2015. "Semi-parametric modeling of excesses above high multivariate thresholds with censored data," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 126-146.
    6. de Carvalho, Miguel & Oumow, Boris & Segers, Johan & WarchoÅ‚, MichaÅ‚, 2012. "A Euclidean likelihood estimator for bivariate tail dependence," LIDAM Discussion Papers ISBA 2012013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Deyuan Li & Liang Peng & Yongcheng Qi, 2011. "Empirical likelihood confidence intervals for the endpoint of a distribution function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 353-366, August.
    8. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Bücher, Axel & Volgushev, Stanislav & Zou, Nan, 2019. "On second order conditions in the multivariate block maxima and peak over threshold method," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 604-619.
    10. Einmahl, J.H.J. & de Haan, L.F.M. & Krajina, A., 2009. "Estimating Extreme Bivariate Quantile Regions," Other publications TiSEM 007ce0a9-dd94-4301-ad62-1, Tilburg University, School of Economics and Management.
    11. M. Ghil & Pascal Yiou & Stéphane Hallegatte & B. D. Malamud & P. Naveau & A. Soloviev & P. Friederichs & V. Keilis-Borok & D. Kondrashov & V. Kossobokov & O. Mestre & C. Nicolis & H. W. Rust & P. Sheb, 2011. "Extreme events: dynamics, statistics and prediction," Post-Print hal-00716514, HAL.
    12. Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.
    13. Sabourin, Anne & Naveau, Philippe, 2014. "Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 542-567.
    14. Kiriliouk, Anna & Segers, Johan & Warchol, Michal, 2014. "Nonparametric estimation of extremal dependence," LIDAM Discussion Papers ISBA 2014044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Cui, Hengxin & Tan, Ken Seng & Yang, Fan & Zhou, Chen, 2022. "Asymptotic analysis of portfolio diversification," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 302-325.
    16. Shi, Xiaojun & Tang, Qihe & Yuan, Zhongyi, 2017. "A limit distribution of credit portfolio losses with low default probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 156-167.
    17. Khader Khadraoui & Pierre Ribereau, 2019. "Bayesian Inference with M-splines on Spectral Measure of Bivariate Extremes," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 765-788, September.

  19. Einmahl, J.H.J. & Gantner, M. & Sawitzki, G., 2008. "The Shorth Plot," Discussion Paper 2008-24, Tilburg University, Center for Economic Research.

    Cited by:

    1. Aigner, Maximilian & Chavez-Demoulin, Valérie & Guillou, Armelle, 2022. "Measuring and comparing risks of different types," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 1-21.
    2. Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2014. "Statistics of Heteroscedastic Extremes," Other publications TiSEM 19952ae4-25ff-4e1b-8627-d, Tilburg University, School of Economics and Management.
    3. Einmahl, John & Segers, Johan, 2020. "Empirical Tail Copulas for Functional Data," Discussion Paper 2020-004, Tilburg University, Center for Economic Research.

  20. Einmahl, J.H.J. & Krajina, A. & Segers, J.J.J., 2007. "A Method of Moments Estimator of Tail Dependence," Discussion Paper 2007-80, Tilburg University, Center for Economic Research.

    Cited by:

    1. Einmahl, John H. J. & Krajina, Andrea & Segers, Johan, 2012. "An M-estimator for tail dependence in arbitrary dimensions," LIDAM Reprints ISBA 2012035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Di Bernardino, Elena & Maume-Deschamps, Véronique & Prieur, Clémentine, 2013. "Estimating a bivariate tail: A copula based approach," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 81-100.
    3. Krajina, A., 2009. "A Method of Moments Estimator of Tail Dependence in Elliptical Copula Models," Discussion Paper 2009-42, Tilburg University, Center for Economic Research.
    4. Fougères, Anne-Laure & Mercadier, Cécile & Nolan, John P., 2013. "Dense classes of multivariate extreme value distributions," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 109-129.
    5. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "Beyond dimension two: A test for higher-order tail risk," SFB 649 Discussion Papers SFB649DP2014-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. John H. J. Einmahl & Anna Kiriliouk & Andrea Krajina & Johan Segers, 2016. "An M-estimator of spatial tail dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 275-298, January.
    7. Einmahl, J.H.J. & Segers, J.J.J., 2009. "Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution," Other publications TiSEM ffef2e15-c4a8-471f-b730-1, Tilburg University, School of Economics and Management.
    8. Kiriliouk, Anna & Lee, Jeongjin & Segers, Johan, 2023. "X-Vine Models for Multivariate Extremes," LIDAM Discussion Papers ISBA 2023038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    10. Bücher Axel, 2014. "A note on nonparametric estimation of bivariate tail dependence," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 1-12, June.
    11. Matthieu Garcin & Maxime L. D. Nicolas, 2021. "Nonparametric estimator of the tail dependence coefficient: balancing bias and variance," Papers 2111.11128, arXiv.org, revised Jul 2023.
    12. Lorenzo Ricci & David Veredas, 2012. "TailCoR," Working Papers 1227, Banco de España.
      • Sla{dj}ana Babi'c & Christophe Ley & Lorenzo Ricci & David Veredas, 2020. "TailCoR," Papers 2011.14817, arXiv.org.
    13. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    14. Michael Falk & Gilles Stupfler, 2021. "The Min-characteristic Function: Characterizing Distributions by Their Min-linear Projections," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 254-282, February.
    15. Krajina, A., 2009. "A Method of Moments Estimator of Tail Dependence in Elliptical Copula Models," Other publications TiSEM f3f5a961-02ff-4a2b-ab93-4, Tilburg University, School of Economics and Management.

  21. Beirlant, J. & Einmahl, J.H.J., 2007. "Asymptotics for the Hirsch Index," Discussion Paper 2007-86, Tilburg University, Center for Economic Research.

    Cited by:

    1. Krisztina Barcza & András Telcs, 2009. "Paretian publication patterns imply Paretian Hirsch index," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(2), pages 513-519, November.
    2. Paola Cerchiello & Paolo Giudici, 2014. "How to measure the quality of financial tweets," DEM Working Papers Series 069, University of Pavia, Department of Economics and Management.
    3. Paola Cerchiello & Paolo Giudici, 2014. "Financial big data analysis for the estimation of systemic risks," DEM Working Papers Series 086, University of Pavia, Department of Economics and Management.
    4. J. Martin Zyl, 2013. "The generalized Pareto distribution fitted to research outputs of countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1099-1109, March.
    5. Baccini, A. & Barabesi, L. & Marcheselli, M. & Pratelli, L., 2012. "Statistical inference on the h-index with an application to top-scientist performance," Journal of Informetrics, Elsevier, vol. 6(4), pages 721-728.
    6. Panaretos, John & Malesios, Chrisovaladis, 2008. "Assessing scientific research performance and impact with single indices," MPRA Paper 12842, University Library of Munich, Germany.
    7. Wolfgang Glänzel & Henk F. Moed, 2013. "Opinion paper: thoughts and facts on bibliometric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 381-394, July.
    8. Wolfgang Glänzel, 2010. "The role of the h-index and the characteristic scores and scales in testing the tail properties of scientometric distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(3), pages 697-709, June.
    9. Paola Cerchiello & Paolo Giudici, 2013. "H Index: A Statistical Proposal," DEM Working Papers Series 039, University of Pavia, Department of Economics and Management.
    10. Paola Cerchiello & Paolo Giudici, 2015. "A Bayesian h-index: how to measure research impact," DEM Working Papers Series 102, University of Pavia, Department of Economics and Management.
    11. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    12. Paola Cerchiello & Paolo Giudici, 2014. "On a statistical h index," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 299-312, May.

  22. Einmahl, J.H.J. & Khmaladze, E.V., 2007. "Central Limit Theorems For Local Emprical Processes Near Boundaries of Sets," Discussion Paper 2007-66, Tilburg University, Center for Economic Research.

    Cited by:

    1. Einmahl, J.H.J. & Khmaladze, E.V., 2007. "Central Limit Theorems For Local Emprical Processes Near Boundaries of Sets," Other publications TiSEM c4c26f2d-99d3-473f-9900-e, Tilburg University, School of Economics and Management.

  23. Einmahl, J.H.J. & Magnus, J.R., 2006. "Records in Athletics through Extreme-Value Theory," Discussion Paper 2006-83, Tilburg University, Center for Economic Research.

    Cited by:

    1. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    2. Ray C. Fair & Edward H. Kaplan, 2017. "Estimating Aging Effects in Running Events," Cowles Foundation Discussion Papers 2100, Cowles Foundation for Research in Economics, Yale University.
    3. Russell Brook T. & Hogan Paul, 2018. "Analyzing dependence matrices to investigate relationships between national football league combine event performances," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(4), pages 201-212, December.
    4. Christina Empacher & Udo Kamps & Grigoriy Volovskiy, 2023. "Statistical Prediction of Future Sports Records Based on Record Values," Stats, MDPI, vol. 6(1), pages 1-17, January.
    5. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    6. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.
    7. Hong En Tan & De Wen Soh & Yong Sheng Soh & Muhamad Azfar Ramli, 2021. "Derivation of train arrival timings through correlations from individual passenger farecard data," Transportation, Springer, vol. 48(6), pages 3181-3205, December.
    8. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    9. Wang, Bing Xing & Yu, Keming & Coolen, Frank P.A., 2015. "Interval estimation for proportional reversed hazard family based on lower record values," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 115-122.
    10. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.
    11. Shaul Bar-Lev, 2008. "Point and confidence interval estimates for a global maximum via extreme value theory," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(12), pages 1371-1381.
    12. M. Ivette Gomes & Armelle Guillou, 2015. "Extreme Value Theory and Statistics of Univariate Extremes: A Review," International Statistical Review, International Statistical Institute, vol. 83(2), pages 263-292, August.
    13. Daoud, Adel, 2018. "Unifying Studies of Scarcity, Abundance, and Sufficiency," Ecological Economics, Elsevier, vol. 147(C), pages 208-217.
    14. Gbari, Kock Yed Ake Samuel & Poulain, Michel & Dal, Luc & Denuit, Michel, 2016. "Extreme value analysis of mortality at the oldest ages: a case study based on individual ages at death," LIDAM Discussion Papers ISBA 2016012, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  24. Einmahl, J.H.J. & de Haan, L.F.M. & Li, D., 2006. "Weighted approximations of tail copula processes with applications to testing the bivariate extreme value condition," Other publications TiSEM 18b65ac3-ba79-4bff-ad53-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    2. Einmahl, J.H.J. & Krajina, A. & Segers, J.J.J., 2007. "A Method of Moments Estimator of Tail Dependence," Discussion Paper 2007-80, Tilburg University, Center for Economic Research.
    3. Di Bernardino, Elena & Maume-Deschamps, Véronique & Prieur, Clémentine, 2013. "Estimating a bivariate tail: A copula based approach," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 81-100.
    4. de Haan, Laurens & Neves, Cláudia & Peng, Liang, 2008. "Parametric tail copula estimation and model testing," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1260-1275, July.
    5. Krajina, A., 2009. "A Method of Moments Estimator of Tail Dependence in Elliptical Copula Models," Discussion Paper 2009-42, Tilburg University, Center for Economic Research.
    6. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "Beyond dimension two: A test for higher-order tail risk," SFB 649 Discussion Papers SFB649DP2014-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Sun, Hongfang & Chen, Yu & Hu, Taizhong, 2022. "Statistical inference for tail-based cumulative residual entropy," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 66-95.
    8. Juan-Juan Cai & John H. J. Einmahl & Laurens Haan & Chen Zhou, 2015. "Estimation of the marginal expected shortfall: the mean when a related variable is extreme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 417-442, March.
    9. Einmahl, J.H.J. & Li, J. & Liu, R.Y., 2006. "Extreme Value Theory Approach to Simultaneous Monitoring and Thresholding of Multiple Risk Indicators," Other publications TiSEM 4e0aab6a-b885-4a21-a898-2, Tilburg University, School of Economics and Management.
    10. Kiriliouk, Anna & Segers, Johan & Tafakori, Laleh, 2018. "An estimator of the stable tail dependence function based on the empirical beta copula," LIDAM Discussion Papers ISBA 2018029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Durante, Fabrizio & Foschi, Rachele & Spizzichino, Fabio, 2008. "Threshold copulas and positive dependence," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 2902-2909, December.
    12. Claudia Klüppelberg & Gabriel Kuhn & Liang Peng, 2008. "Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 701-718, December.
    13. Jäschke, Stefan, 2014. "Estimation of risk measures in energy portfolios using modern copula techniques," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 359-376.
    14. Moosup Kim & Sangyeol Lee, 2022. "Maximum composite likelihood estimation for spatial extremes models of Brown–Resnick type with application to precipitation data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1023-1059, September.
    15. Benchaira, Souad & Meraghni, Djamel & Necir, Abdelhakim, 2015. "On the asymptotic normality of the extreme value index for right-truncated data," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 378-384.
    16. Bücher Axel, 2014. "A note on nonparametric estimation of bivariate tail dependence," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 1-12, June.
    17. Rootzen, Holger & Segers, Johan & Wadsworth, Jennifer, 2017. "Multivariate generalized Pareto distributions: parametrizations, representations, and properties," LIDAM Discussion Papers ISBA 2017016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    18. Stefan Aulbach & Michael Falk & Timo Fuller, 2019. "Testing for a $$\delta $$ δ -neighborhood of a generalized Pareto copula," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 599-626, June.
    19. Maarten van Oordt & Chen Zhou, 2016. "Estimating Systematic Risk Under Extremely Adverse Market Conditions," Staff Working Papers 16-22, Bank of Canada.
    20. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    21. Kiriliouk, Anna & Segers, Johan & Tafakori, Laleh, 2017. "An estimator of the stable tail dependence function based on the empirical beta copula," LIDAM Discussion Papers ISBA 2017028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    22. Wu, Lifan & Samorodnitsky, Gennady, 2020. "Regularly varying random fields," Stochastic Processes and their Applications, Elsevier, vol. 130(7), pages 4470-4492.
    23. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "A Test for the Portion of Bivariate Dependence in Multivariate Tail Risk," Tinbergen Institute Discussion Papers 14-024/III, Tinbergen Institute, revised 23 Jun 2014.
    24. Kiriliouk, Anna & Segers, Johan & Warchol, Michal, 2014. "Nonparametric estimation of extremal dependence," LIDAM Discussion Papers ISBA 2014044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    25. Rootzén, Holger & Segers, Johan & Wadsworth, Jennifer L., 2018. "Multivariate generalized Pareto distributions: Parametrizations, representations, and properties," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 117-131.
    26. Yannick Hoga, 2023. "The Estimation Risk in Extreme Systemic Risk Forecasts," Papers 2304.10349, arXiv.org.
    27. Krajina, A., 2009. "A Method of Moments Estimator of Tail Dependence in Elliptical Copula Models," Other publications TiSEM f3f5a961-02ff-4a2b-ab93-4, Tilburg University, School of Economics and Management.
    28. Khader Khadraoui & Pierre Ribereau, 2019. "Bayesian Inference with M-splines on Spectral Measure of Bivariate Extremes," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 765-788, September.
    29. Bucher, Axel & Jaschke, Stefan & Wied, Dominik, 2013. "Nonparametric tests for constant tail dependence with an application to energy and finance," LIDAM Discussion Papers ISBA 2013033, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  25. Einmahl, J.H.J. & van Keilegom, I., 2006. "Tests for Independence in Nonparametric Regression," Discussion Paper 2006-80, Tilburg University, Center for Economic Research.

    Cited by:

    1. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    2. Mastromarco, Camilla & Simar, Leopold, 2018. "Globalization and productivity: A robust nonparametric world frontier analysis," LIDAM Reprints ISBA 2018008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Florens, Jean-Pierre & Simar, Leopold & Van Keilegom, Ingrid, 2013. "Frontier estimation in nonparametric location-scale models," LIDAM Reprints ISBA 2013035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Braekers, Roel & Van Keilegom, Ingrid, 2009. "Flexible modeling based on copulas in nonparametric median regression," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1270-1281, July.
    5. Einmahl, J.H.J. & van Keilegom, I., 2004. "Goodness-of-fit Tests in Nonparametric Regression," Discussion Paper 2004-12, Tilburg University, Center for Economic Research.
    6. Neumeyer, Natalie, 2009. "Testing independence in nonparametric regression," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1551-1566, August.
    7. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2012. "n-uniformly consistent density estimation in nonparametric regression models," Journal of Econometrics, Elsevier, vol. 167(2), pages 305-316.
    8. Van Keilegom, Ingrid, 2013. "Discussion on: "An updated review of Goodness-of-Fit tests for regression models" (by W. Gonzales-Manteiga and R.M. Crujeiras)," LIDAM Discussion Papers ISBA 2013008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Fan, Caiyun & Lu, Wenbin & Zhou, Yong, 2021. "Testing error heterogeneity in censored linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    10. Simar, Leopold & Vanhems, Anne & Van Keilegom, Ingrid, 2016. "Unobserved heterogeneity and endogeneity in nonparametric frontier estimation," LIDAM Reprints ISBA 2016007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Sankar, Subhra & Bergsma, Wicher & Dassios, Angelos, 2017. "Testing independence of covariates and errors in nonparametric regression," LSE Research Online Documents on Economics 83780, London School of Economics and Political Science, LSE Library.
    12. Hlávka, Zdenek & Husková, Marie & Meintanis, Simos G., 2011. "Tests for independence in non-parametric heteroscedastic regression models," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 816-827, April.
    13. Jun Zhang & Zhenghui Feng & Peirong Xu, 2015. "Estimating the conditional single-index error distribution with a partial linear mean regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 61-83, March.
    14. Teran Hidalgo, Sebastian J. & Wu, Michael C. & Engel, Stephanie M. & Kosorok, Michael R., 2018. "Goodness-of-fit test for nonparametric regression models: Smoothing spline ANOVA models as example," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 135-155.

  26. Einmahl, J.H.J. & van Keilegom, I., 2004. "Goodness-of-fit Tests in Nonparametric Regression," Discussion Paper 2004-12, Tilburg University, Center for Economic Research.

    Cited by:

    1. Sokbae Lee & Oliver Linton & Yoon-Jae Whang, 2009. "Testing for Stochastic Monotonicity," Econometrica, Econometric Society, vol. 77(2), pages 585-602, March.

  27. Einmahl, J.H.J. & Lin, T., 2003. "Asymptotic Normality of Extreme Value Estimators on C[0,1]," Discussion Paper 2003-132, Tilburg University, Center for Economic Research.

    Cited by:

    1. Einmahl, J.H.J. & Khmaladze, E.V., 2007. "Central Limit Theorems For Local Emprical Processes Near Boundaries of Sets," Other publications TiSEM c4c26f2d-99d3-473f-9900-e, Tilburg University, School of Economics and Management.
    2. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.
    3. Einmahl, John & Segers, Johan, 2020. "Empirical Tail Copulas for Functional Data," Discussion Paper 2020-004, Tilburg University, Center for Economic Research.
    4. Robert, Christian Y., 2022. "Testing for changes in the tail behavior of Brown–Resnick Pareto processes," Stochastic Processes and their Applications, Elsevier, vol. 144(C), pages 312-368.

  28. Einmahl, J.H.J. & McKeague, I.W., 2002. "Empirical Likelihood based on Hypothesis Testing," Discussion Paper 2002-92, Tilburg University, Center for Economic Research.

    Cited by:

    1. Yukitoshi Matsushita & Taisuke Otsu & Ke-Li Xu, 2014. "Empirical Likelihood for Regression Discontinuity Design," STICERD - Econometrics Paper Series 573, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Xiaohui Liu & Qihua Wang & Yi Liu, 2017. "A consistent jackknife empirical likelihood test for distribution functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 249-269, April.
    3. Gantner, M., 2010. "Some nonparametric diagnostic statistical procedures and their asymptotic behavior," Other publications TiSEM eb04bdba-bf8a-4f6c-8dd8-9, Tilburg University, School of Economics and Management.
    4. Hammou El Barmi & Lahcen El Bermi, 2013. "Empirical likelihood ratio test for symmetry against type I bias with applications to competing risks," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 487-498, June.
    5. Shen, Gang, 2013. "On empirical likelihood inference of a change-point," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1662-1668.
    6. Zhang, Jin & Wu, Yuehua, 2007. "k-Sample tests based on the likelihood ratio," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4682-4691, May.
    7. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.
    8. Karun Adusumilli & Taisuke Otsu, 2017. "Empirical Likelihood for Random Sets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1064-1075, July.
    9. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Discussion Paper 2023-001, Tilburg University, Center for Economic Research.
    10. Zou, Changliang & Liu, Yukun & Qin, Peng & Wang, Zhaojun, 2007. "Empirical likelihood ratio test for the change-point problem," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 374-382, February.
    11. Einmahl, J.H.J. & Gantner, M., 2012. "Testing for bivariate spherical symmetry," Other publications TiSEM f02b446f-b69b-45bb-b39d-2, Tilburg University, School of Economics and Management.
    12. Fernández-Durán Juan José & Gregorio-Domínguez María Mercedes, 2023. "Test of bivariate independence based on angular probability integral transform with emphasis on circular-circular and circular-linear data," Dependence Modeling, De Gruyter, vol. 11(1), pages 1-17, January.
    13. Narayanaswamy Balakrishnan & Laurent Bordes & Christian Paroissin & Jean-Christophe Turlot, 2016. "Single change-point detection methods for small lifetime samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(5), pages 531-551, July.
    14. Rodríguez-Martínez, C.M. & Coronel-Brizio, H.F. & Hernández-Montoya, A.R., 2021. "A multi-scale symmetry analysis of uninterrupted trends returns in daily financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    15. Haywood, John & Khmaladze, Estate, 2008. "On distribution-free goodness-of-fit testing of exponentiality," Journal of Econometrics, Elsevier, vol. 143(1), pages 5-18, March.
    16. Kiwitt, Sebastian & Nagel, Eva-Renate & Neumeyer, Natalie, 2005. "Empirical likelihood estimators for the error distribution in nonparametric regression models," Technical Reports 2005,45, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    17. Hui, Wallace & Gel, Yulia R. & Gastwirth, Joseph L., 2008. "lawstat: An R Package for Law, Public Policy and Biostatistics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i03).
    18. Šárka Hudecová & Marie Hušková & Simos G. Meintanis, 2017. "Tests for Structural Changes in Time Series of Counts," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 843-865, December.
    19. Bagkavos, D. & Patil, P.N., 2017. "A new test of independence for bivariate observations," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 117-133.
    20. Song Xi Chen & Jiti Gao, 2010. "Simultaneous Testing of Mean and Variance Structures in Nonlinear Time Series Models," School of Economics and Public Policy Working Papers 2010-28, University of Adelaide, School of Economics and Public Policy.
    21. Albert Vexler & Wan-Min Tsai & Alan D. Hutson, 2014. "A Simple Density-Based Empirical Likelihood Ratio Test for Independence," The American Statistician, Taylor & Francis Journals, vol. 68(3), pages 158-169, February.
    22. C. M. Rodr'iguez-Mart'inez & H. F. Coronel-Brizio & A. R. Hern'andez-Montoya, 2019. "A multi-scale symmetry analysis of uninterrupted trends returns of daily financial indices," Papers 1908.11204, arXiv.org.
    23. Baringhaus, Ludwig & Gaigall, Daniel, 2015. "On an independence test approach to the goodness-of-fit problem," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 193-208.
    24. Koning, A.J. & Peng, L., 2005. "Goodness-of-fit tests for a heavy tailed distribution," Econometric Institute Research Papers EI 2005-44, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    25. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos Meintanis, 2012. "Monitoring changes in the error distribution of autoregressive models based on Fourier methods," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 605-634, December.
    26. Coronel-Brizio, H.F. & Hernández-Montoya, A.R. & Huerta-Quintanilla, R. & Rodríguez-Achach, M., 2007. "Assessing symmetry of financial returns series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(1), pages 5-9.

  29. Einmahl, J.H.J. & Khmaladze, E.V., 2001. "The two-sample problem in Rm and measure-valued martingales," Other publications TiSEM 05e25f02-c6e7-4e9c-b42b-f, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, J.H.J. & McKeague, I.W., 2003. "Empirical likelihood based hypothesis testing," Other publications TiSEM 2ddb34d8-8ae7-46e3-8004-c, Tilburg University, School of Economics and Management.
    2. Khmaladze, E.V., 2012. "On Distribution Free Test for Discrete Distributions and an Extension to Continuous Time," Other publications TiSEM 2296a9e4-651b-472d-aa2e-7, Tilburg University, School of Economics and Management.
    3. Khmaladze, E.V., 2012. "On Distribution Free Test for Discrete Distributions and an Extension to Continuous Time," Discussion Paper 2012-028, Tilburg University, Center for Economic Research.
    4. Can, S.U. & Einmahl, John & Laeven, R.J.A., 2020. "Goodness-of-fit testing for copulas: A distribution-free approach," Other publications TiSEM 211b2be9-b46e-41e2-9b95-1, Tilburg University, School of Economics and Management.

  30. Einmahl, J.H.J. & de Haan, L.F.M. & Piterbarg, V.I., 2001. "Nonparametric estimation of the spectral measure of an extreme value distribution," Other publications TiSEM c3485b9b-a0bd-456f-9baa-0, Tilburg University, School of Economics and Management.

    Cited by:

    1. Padoan, Simone A., 2011. "Multivariate extreme models based on underlying skew-t and skew-normal distributions," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 977-991, May.
    2. Goix, Nicolas & Sabourin, Anne & Clémençon, Stephan, 2017. "Sparse representation of multivariate extremes with applications to anomaly detection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 12-31.
    3. Einmahl, J.H.J. & de Haan, L.F.M. & Li, D., 2006. "Weighted approximations of tail copula processes with applications to testing the bivariate extreme value condition," Other publications TiSEM 18b65ac3-ba79-4bff-ad53-2, Tilburg University, School of Economics and Management.
    4. Keef, Caroline & Papastathopoulos, Ioannis & Tawn, Jonathan A., 2013. "Estimation of the conditional distribution of a multivariate variable given that one of its components is large: Additional constraints for the Heffernan and Tawn model," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 396-404.
    5. Georg Mainik & Ludger Rüschendorf, 2010. "On optimal portfolio diversification with respect to extreme risks," Finance and Stochastics, Springer, vol. 14(4), pages 593-623, December.
    6. Einmahl, J.H.J. & Segers, J.J.J., 2009. "Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution," Other publications TiSEM ffef2e15-c4a8-471f-b730-1, Tilburg University, School of Economics and Management.
    7. Cooley, Daniel & Davis, Richard A. & Naveau, Philippe, 2010. "The pairwise beta distribution: A flexible parametric multivariate model for extremes," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2103-2117, October.
    8. Sabourin, Anne, 2015. "Semi-parametric modeling of excesses above high multivariate thresholds with censored data," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 126-146.
    9. Einmahl, J.H.J. & de Haan, L.F.M. & Li, D., 2004. "Weighted Approximations of Tail Copula Processes with Application to Testing the Multivariate Extreme Value Condition," Discussion Paper 2004-71, Tilburg University, Center for Economic Research.
    10. de Carvalho, Miguel & Oumow, Boris & Segers, Johan & WarchoÅ‚, MichaÅ‚, 2012. "A Euclidean likelihood estimator for bivariate tail dependence," LIDAM Discussion Papers ISBA 2012013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Rasmus Pedersen & Olivier Wintenberger, 2017. "On the tail behavior of a class of multivariate conditionally heteroskedastic processes," Papers 1701.05091, arXiv.org, revised Dec 2017.
    12. Basrak, Bojan & Segers, Johan, 2009. "Regularly varying multivariate time series," Stochastic Processes and their Applications, Elsevier, vol. 119(4), pages 1055-1080, April.
    13. Kiriliouk, Anna & Lee, Jeongjin & Segers, Johan, 2023. "X-Vine Models for Multivariate Extremes," LIDAM Discussion Papers ISBA 2023038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    14. Hu, Shuang & Peng, Zuoxiang & Segers, Johan, 2022. "Modelling multivariate extreme value distributions via Markov trees," LIDAM Discussion Papers ISBA 2022021, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Claudia Klüppelberg & Gabriel Kuhn & Liang Peng, 2008. "Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 701-718, December.
    16. Herrera, R. & Eichler, S., 2011. "Extreme dependence with asymmetric thresholds: Evidence for the European Monetary Union," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2916-2930, November.
    17. Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.
    18. Fils-Villetard, A. & Guillou, A. & Segers, J., 2005. "Projection Estimates of Constrained Functional Parameters," Discussion Paper 2005-111, Tilburg University, Center for Economic Research.
    19. Alexandra Ramos & Anthony Ledford, 2009. "A new class of models for bivariate joint tails," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 219-241, January.
    20. Zhang, Dabao & Wells, Martin T. & Peng, Liang, 2008. "Nonparametric estimation of the dependence function for a multivariate extreme value distribution," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 577-588, April.
    21. Estate Khmaladze & Wolfgang Weil, 2008. "Local empirical processes near boundaries of convex bodies," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 813-842, December.
    22. Sabourin, Anne & Naveau, Philippe, 2014. "Bayesian Dirichlet mixture model for multivariate extremes: A re-parametrization," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 542-567.
    23. Kiriliouk, Anna & Segers, Johan & Warchol, Michal, 2014. "Nonparametric estimation of extremal dependence," LIDAM Discussion Papers ISBA 2014044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    24. Cui, Hengxin & Tan, Ken Seng & Yang, Fan & Zhou, Chen, 2022. "Asymptotic analysis of portfolio diversification," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 302-325.
    25. Fils-Villetard, A. & Guillou, A. & Segers, J., 2005. "Projection Estimates of Constrained Functional Parameters," Other publications TiSEM fe25c070-c313-4369-a6a5-8, Tilburg University, School of Economics and Management.
    26. Khader Khadraoui & Pierre Ribereau, 2019. "Bayesian Inference with M-splines on Spectral Measure of Bivariate Extremes," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 765-788, September.

  31. Di Bucchianico, A. & Einmahl, J.H.J. & Mushkudiani, N.A., 2001. "Smallest nonparametric tolerance regions," Other publications TiSEM 436f9be2-d0ad-49af-b6df-9, Tilburg University, School of Economics and Management.

    Cited by:

    1. C. Tsao & Yu-Ling Tseng, 2006. "Confidence estimation for tolerance intervals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 441-456, September.
    2. Kedai Cheng & Derek S. Young, 2023. "An Approach for Specifying Trimming and Winsorization Cutoffs," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 299-323, June.
    3. Algo Carè & Simone Garatti & Marco C. Campi, 2017. "A coverage theory for least squares," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1367-1389, November.
    4. Florens, Jean-Pierre & Simar, Leopold & Van Keilegom, Ingrid, 2013. "Frontier estimation in nonparametric location-scale models," LIDAM Reprints ISBA 2013035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Olive, David J., 2007. "Prediction intervals for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3115-3122, March.
    6. Amparo Baíllo & Antonio Cuevas, 2006. "Parametric versus nonparametric tolerance regions in detection problems," Computational Statistics, Springer, vol. 21(3), pages 523-536, December.
    7. Serfling, Robert, 2002. "Generalized Quantile Processes Based on Multivariate Depth Functions, with Applications in Nonparametric Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 232-247, October.
    8. David J. Olive, 2018. "Applications of hyperellipsoidal prediction regions," Statistical Papers, Springer, vol. 59(3), pages 913-931, September.
    9. Ilaria Lucrezia Amerise, 2023. "A direct method for constructing distribution-free tolerance regions," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 3941-3954, October.
    10. Frey, Jesse, 2014. "Shorter nonparametric prediction intervals for an order statistic from a future sample," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 69-75.
    11. Jesse Frey, 2010. "Data-driven nonparametric tolerance sets," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 169-180.

  32. Einmahl, J.H.J. & Deheuvels, P., 2000. "Functional limit laws for the increments of Kaplan-Meier product-limit processes and applications," Other publications TiSEM ac9bbdc0-62f8-4b48-9a84-1, Tilburg University, School of Economics and Management.

    Cited by:

    1. Paul Deheuvels & David Mason, 2004. "General Asymptotic Confidence Bands Based on Kernel-type Function Estimators," Statistical Inference for Stochastic Processes, Springer, vol. 7(3), pages 225-277, October.
    2. Hamri Mohamed Mehdi & Mekki Sanaà Dounya & Rabhi Abbes & Kadiri Nadia, 2022. "Single Functional Index Quantile Regression for Independent Functional Data Under Right-Censoring," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(1), pages 31-62, March.
    3. Mohamed Lemdani & Elias Ould Saïd, 2017. "Nonparametric robust regression estimation for censored data," Statistical Papers, Springer, vol. 58(2), pages 505-525, June.
    4. Paul Deheuvels & Sarah Ouadah, 2013. "Uniform-in-Bandwidth Functional Limit Laws," Journal of Theoretical Probability, Springer, vol. 26(3), pages 697-721, September.
    5. Guessoum Zohra & Ould-Said Elias, 2009. "On nonparametric estimation of the regression function under random censorship model," Statistics & Risk Modeling, De Gruyter, vol. 26(3), pages 159-177, April.

  33. Einmahl, J.H.J. & McKeague, I.W., 1999. "Confidence tubes for multiple quantile plots via empirical likelihood," Other publications TiSEM b64493f8-1c01-40fd-b16d-7, Tilburg University, School of Economics and Management.

    Cited by:

    1. Subramanian, Sundarraman, 2022. "Simultaneous confidence bands for survival functions from twice censorship," Statistics & Probability Letters, Elsevier, vol. 186(C).
    2. Shihong Zhu & Yifan Yang & Mai Zhou, 2015. "A note on the empirical likelihood confidence band for hazards ratio with covariate adjustment," Biometrics, The International Biometric Society, vol. 71(3), pages 859-563, September.
    3. Boon, M. & Einmahl, J.H.J. & McKeague, I.W., 2011. "Visualizing Multiple Quantile Plots," Discussion Paper 2011-085, Tilburg University, Center for Economic Research.
    4. McKeague, Ian W. & Zhao, Yichuan, 2002. "Simultaneous confidence bands for ratios of survival functions via empirical likelihood," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 405-415, December.
    5. Nubyra Ahmed & Sundarraman Subramanian, 2016. "Semiparametric simultaneous confidence bands for the difference of survival functions," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 504-530, October.
    6. Yichuan Zhao & Song Yang, 2008. "Empirical likelihood inference for censored median regression with weighted empirical hazard functions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(2), pages 441-457, June.
    7. Zhao, Yichuan, 2010. "Semiparametric inference for transformation models via empirical likelihood," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1846-1858, September.
    8. Yang, Hanfang & Zhao, Yichuan, 2012. "Smoothed empirical likelihood for ROC curves with censored data," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 254-263.
    9. Yanqing Sun & Rajeshwari Sundaram & Yichuan Zhao, 2009. "Empirical Likelihood Inference for the Cox Model with Time‐dependent Coefficients via Local Partial Likelihood," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 444-462, September.
    10. Zhou, Mai & Zhu, Shihong, 2015. "Empirical likelihood confidence band for the difference of survival functions under proportional hazards model," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 228-235.
    11. Junshan Shen & Shuyuan He, 2007. "Empirical likelihood for the difference of quantiles under censorship," Statistical Papers, Springer, vol. 48(3), pages 437-457, September.
    12. Khmaladze, E.V., 2012. "On Distribution Free Test for Discrete Distributions and an Extension to Continuous Time," Other publications TiSEM 2296a9e4-651b-472d-aa2e-7, Tilburg University, School of Economics and Management.
    13. Khmaladze, E.V., 2012. "On Distribution Free Test for Discrete Distributions and an Extension to Continuous Time," Discussion Paper 2012-028, Tilburg University, Center for Economic Research.
    14. Zhao, Yichuan & Zhao, Meng, 2011. "Empirical likelihood for the contrast of two hazard functions with right censoring," Statistics & Probability Letters, Elsevier, vol. 81(3), pages 392-401, March.
    15. Subramanian, Sundarraman, 2016. "Bootstrap likelihood ratio confidence bands for survival functions under random censorship and its semiparametric extension," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 58-81.
    16. Robert Drake & Apratim Guha, 2014. "A mutual information-based k -sample test for discrete distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 2011-2027, September.
    17. Shen, Junshan & He, Shuyuan, 2006. "Empirical likelihood for the difference of two survival functions under right censorship," Statistics & Probability Letters, Elsevier, vol. 76(2), pages 169-181, January.
    18. Jose Blanchet & Yang Kang, 2021. "Sample Out-of-Sample Inference Based on Wasserstein Distance," Operations Research, INFORMS, vol. 69(3), pages 985-1013, May.
    19. Cheng, Guang & Zhao, Yichuan & Li, Bo, 2012. "Empirical likelihood inferences for the semiparametric additive isotonic regression," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 172-182.
    20. Marc S. Paolella, 2015. "New Graphical Methods and Test Statistics for Testing Composite Normality," Econometrics, MDPI, vol. 3(3), pages 1-29, July.
    21. McKeague, Ian W. & Zhao, Yichuan, 2006. "Width-scaled confidence bands for survival functions," Statistics & Probability Letters, Elsevier, vol. 76(4), pages 327-339, February.
    22. Zhao, Yichuan & Chen, Feiming, 2008. "Empirical likelihood inference for censored median regression model via nonparametric kernel estimation," Journal of Multivariate Analysis, Elsevier, vol. 99(2), pages 215-231, February.

  34. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, John H. J. & Krajina, Andrea & Segers, Johan, 2012. "An M-estimator for tail dependence in arbitrary dimensions," LIDAM Reprints ISBA 2012035, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
    3. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP44/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Einmahl, John H.J. & de Haan, Laurens & Sinha, Ashoke Kumar, 1997. "Estimating the spectral measure of an extreme value distribution," Stochastic Processes and their Applications, Elsevier, vol. 70(2), pages 143-171, October.
    5. Beirlant, J. & Einmahl, J.H.J., 2007. "Asymptotics for the Hirsch Index," Discussion Paper 2007-86, Tilburg University, Center for Economic Research.
    6. Einmahl, J.H.J. & de Haan, L.F.M. & Piterbarg, V.I., 2001. "Nonparametric estimation of the spectral measure of an extreme value distribution," Other publications TiSEM c3485b9b-a0bd-456f-9baa-0, Tilburg University, School of Economics and Management.
    7. Einmahl, J.H.J. & Segers, J.J.J., 2009. "Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution," Other publications TiSEM ffef2e15-c4a8-471f-b730-1, Tilburg University, School of Economics and Management.
    8. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Other publications TiSEM 261583f5-c571-48c6-8cea-9, Tilburg University, School of Economics and Management.
    9. Einmahl, John & Krajina, Andrea, 2023. "Empirical Likelihood Based Testing for Multivariate Regular Variation," Discussion Paper 2023-001, Tilburg University, Center for Economic Research.
    10. Dümbgen, Lutz & Wellner, Jon A. & Wolff, Malcolm, 2016. "A law of the iterated logarithm for Grenander’s estimator," Stochastic Processes and their Applications, Elsevier, vol. 126(12), pages 3854-3864.
    11. Claudia Klüppelberg & Gabriel Kuhn & Liang Peng, 2008. "Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 701-718, December.
    12. Einmahl, J.H.J. & Khmaladze, E.V., 2007. "Central Limit Theorems For Local Emprical Processes Near Boundaries of Sets," Other publications TiSEM c4c26f2d-99d3-473f-9900-e, Tilburg University, School of Economics and Management.
    13. Chan, Ngai-Hang & Lee, Thomas C.M. & Peng, Liang, 2010. "On nonparametric local inference for density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 509-515, February.
    14. Estate Khmaladze & Wolfgang Weil, 2018. "Fold-up derivatives of set-valued functions and the change-set problem: A Survey," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 1-38, February.
    15. Uwe Einmahl & David M. Mason, 2000. "An Empirical Process Approach to the Uniform Consistency of Kernel-Type Function Estimators," Journal of Theoretical Probability, Springer, vol. 13(1), pages 1-37, January.
    16. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    17. D’Haultfœuille, Xavier & Hoderlein, Stefan & Sasaki, Yuya, 2023. "Nonparametric difference-in-differences in repeated cross-sections with continuous treatments," Journal of Econometrics, Elsevier, vol. 234(2), pages 664-690.
    18. Estate Khmaladze & Wolfgang Weil, 2008. "Local empirical processes near boundaries of convex bodies," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 813-842, December.
    19. Einmahl, J.H.J. & Deheuvels, P., 2000. "Functional limit laws for the increments of Kaplan-Meier product-limit processes and applications," Other publications TiSEM ac9bbdc0-62f8-4b48-9a84-1, Tilburg University, School of Economics and Management.
    20. Chiapino, Mael & Sabourin, Anne & Segers, Johan, 2018. "Identifying groups of variables with the potential of being large simultaneously," LIDAM Discussion Papers ISBA 2018006, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

  35. J Einmahl, .H. & de Haan, L. & Sinha, A., 1997. "Estimating the spectral measure of an extreme value distribution," Other publications TiSEM ac22e123-1e5d-448a-981e-a, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
    2. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.
    3. Einmahl, J.H.J. & de Haan, L.F.M. & Piterbarg, V.I., 2001. "Nonparametric estimation of the spectral measure of an extreme value distribution," Other publications TiSEM c3485b9b-a0bd-456f-9baa-0, Tilburg University, School of Economics and Management.
    4. Capéraà, Philippe & Fougères, Anne-Laure & Genest, Christian, 2000. "Bivariate Distributions with Given Extreme Value Attractor," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 30-49, January.
    5. Falk, Michael & Reiss, Rolf-Dieter, 2005. "On Pickands coordinates in arbitrary dimensions," Journal of Multivariate Analysis, Elsevier, vol. 92(2), pages 426-453, February.
    6. René Michel, 2009. "Parametric Estimation Procedures in Multivariate Generalized Pareto Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 60-75, March.
    7. Georg Mainik & Ludger Rüschendorf, 2010. "On optimal portfolio diversification with respect to extreme risks," Finance and Stochastics, Springer, vol. 14(4), pages 593-623, December.
    8. Einmahl, J.H.J. & Segers, J.J.J., 2009. "Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution," Other publications TiSEM ffef2e15-c4a8-471f-b730-1, Tilburg University, School of Economics and Management.
    9. A. Dematteo & S. Clémençon, 2016. "On tail index estimation based on multivariate data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 152-176, March.
    10. J. L. Wadsworth & J. A. Tawn & A. C. Davison & D. M. Elton, 2017. "Modelling across extremal dependence classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 149-175, January.
    11. Sami Umut Can & John H. J. Einmahl & Roger J. A. Laeven, 2024. "Two-Sample Testing for Tail Copulas with an Application to Equity Indices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(1), pages 147-159, January.
    12. Tim Bollerslev & Viktor Todorov, 2010. "Jump Tails, Extreme Dependencies, and the Distribution of Stock Returns," CREATES Research Papers 2010-64, Department of Economics and Business Economics, Aarhus University.
    13. Lehtomaa, Jaakko & Resnick, Sidney I., 2020. "Asymptotic independence and support detection techniques for heavy-tailed multivariate data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 262-277.
    14. Frahm, Gabriel & Junker, Markus & Schmidt, Rafael, 2005. "Estimating the tail-dependence coefficient: Properties and pitfalls," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 80-100, August.
    15. Mikael Escobar-Bach & Yuri Goegebeur & Armelle Guillou & Alexandre You, 2017. "Bias-corrected and robust estimation of the bivariate stable tail dependence function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(2), pages 284-307, June.
    16. Falk, Michael & Reiss, Rolf-Dieter, 2005. "On the distribution of Pickands coordinates in bivariate EV and GP models," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 267-295, April.
    17. Zhang, Dabao & Wells, Martin T. & Peng, Liang, 2008. "Nonparametric estimation of the dependence function for a multivariate extreme value distribution," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 577-588, April.

  36. Einmahl, J.H.J. & Beirlant, J., 1996. "Maximal type test statistics based on conditional processes," Other publications TiSEM d031d073-2eea-4770-9bee-a, Tilburg University, School of Economics and Management.

    Cited by:

    1. Sokbae Lee & Oliver Linton & Yoon-Jae Whang, 2009. "Testing for Stochastic Monotonicity," Econometrica, Econometric Society, vol. 77(2), pages 585-602, March.

  37. Einmahl, J.H.J., 1996. "Extension to higher dimensions of the Jaeschke-Eicker result on the standardized empirical process," Other publications TiSEM e4026f4e-14f7-4c80-9f72-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.
    2. Ledwina, Teresa & Wyłupek, Grzegorz, 2014. "Validation of positive quadrant dependence," Insurance: Mathematics and Economics, Elsevier, vol. 56(C), pages 38-47.
    3. Zhao, Sihai Dave & Cai, T. Tony & Li, Hongzhe, 2017. "Optimal detection of weak positive latent dependence between two sequences of multiple tests," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 169-184.

  38. Einmahl, J.H.J., 1996. "A short and elementary proof of the main Bahadur-Kiefer theorem," Other publications TiSEM bd980f38-c118-4174-9816-8, Tilburg University, School of Economics and Management.

    Cited by:

    1. Guillou, Armelle & Padoan, Simone A. & Rizzelli, Stefano, 2018. "Inference for asymptotically independent samples of extremes," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 114-135.
    2. Arnold Janssen & Andreas Knoch, 2016. "Information bounds for nonparametric estimators of L-functionals and survival functionals under censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(2), pages 195-220, February.
    3. Endre Csáki & Miklós Csörgő & Antónia Földes & Zhan Shi & Ričardas Zitikis, 2002. "Pointwise and Uniform Asymptotics of the Vervaat Error Process," Journal of Theoretical Probability, Springer, vol. 15(4), pages 845-875, October.

  39. Einmahl, J.H.J. & Deheuvels, P, 1996. "On the strong limiting behavior of local functionals of empirical processes based upon censored data," Other publications TiSEM eac4a4cd-81ee-4107-8c70-a, Tilburg University, School of Economics and Management.

    Cited by:

    1. Zhou, Yong & Yip, Paul S. F., 1999. "A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data," Journal of Multivariate Analysis, Elsevier, vol. 69(2), pages 261-280, May.
    2. Einmahl, J.H.J. & Deheuvels, P., 2000. "Functional limit laws for the increments of Kaplan-Meier product-limit processes and applications," Other publications TiSEM ac9bbdc0-62f8-4b48-9a84-1, Tilburg University, School of Economics and Management.

  40. Einmahl, J.H.J. & Ruymgaart, F.H., 1995. "Tail processes under heavy random censorship with applications," Other publications TiSEM a77f6162-4e20-4e5f-8250-9, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.

  41. Einmahl, J.H.J. & Beirlant, J., 1995. "Asymptotic confidence intervals for the length of the shortt under random censoring," Other publications TiSEM 9772fa7d-a855-4695-b5d4-b, Tilburg University, School of Economics and Management.

    Cited by:

    1. Di Bucchianico, A. & Einmahl, J.H.J. & Mushkudiani, N.A., 2001. "Smallest nonparametric tolerance regions," Other publications TiSEM 436f9be2-d0ad-49af-b6df-9, Tilburg University, School of Economics and Management.

  42. Einmahl, J.H.J. & de Haan, L. & Xin, H., 1993. "Estimating a multidimensional extreme-value distribution," Other publications TiSEM 2816eb0c-8f15-4111-94f5-6, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, John H.J. & de Haan, Laurens & Sinha, Ashoke Kumar, 1997. "Estimating the spectral measure of an extreme value distribution," Stochastic Processes and their Applications, Elsevier, vol. 70(2), pages 143-171, October.
    2. Capéraà, Philippe & Fougères, Anne-Laure & Genest, Christian, 2000. "Bivariate Distributions with Given Extreme Value Attractor," Journal of Multivariate Analysis, Elsevier, vol. 72(1), pages 30-49, January.
    3. Bücher Axel, 2014. "A note on nonparametric estimation of bivariate tail dependence," Statistics & Risk Modeling, De Gruyter, vol. 31(2), pages 1-12, June.
    4. Drees, Holger & Huang, Xin, 1998. "Best Attainable Rates of Convergence for Estimators of the Stable Tail Dependence Function," Journal of Multivariate Analysis, Elsevier, vol. 64(1), pages 25-47, January.
    5. Frahm, Gabriel & Junker, Markus & Schmidt, Rafael, 2005. "Estimating the tail-dependence coefficient: Properties and pitfalls," Insurance: Mathematics and Economics, Elsevier, vol. 37(1), pages 80-100, August.

  43. Einmahl, J.H.J., 1992. "Limit theorems for tail processes with application to intermediate quantile estimation," Other publications TiSEM 063e51b0-445d-4764-96a2-4, Tilburg University, School of Economics and Management.

    Cited by:

    1. Marc Henry & Koen Jochmans & Bernard Salanié, 2014. "Inference on Mixtures Under Tail Restrictions," SciencePo Working papers Main hal-01053810, HAL.
    2. Koen Jochmans & Marc Henry & Bernard Salanié, 2017. "Inference on Two-Component Mixtures under Tail Restrictions," SciencePo Working papers Main hal-03945858, HAL.
    3. Einmahl, J.H.J. & de Haan, L.F.M. & Piterbarg, V.I., 2001. "Nonparametric estimation of the spectral measure of an extreme value distribution," Other publications TiSEM c3485b9b-a0bd-456f-9baa-0, Tilburg University, School of Economics and Management.
    4. Einmahl, J.H.J. & de Haan, L.F.M. & Li, D., 2006. "Weighted approximations of tail copula processes with applications to testing the bivariate extreme value condition," Other publications TiSEM 18b65ac3-ba79-4bff-ad53-2, Tilburg University, School of Economics and Management.
    5. Kulik, Rafal & Soulier, Philippe, 2011. "The tail empirical process for long memory stochastic volatility sequences," Stochastic Processes and their Applications, Elsevier, vol. 121(1), pages 109-134, January.
    6. Einmahl, J.H.J. & de Haan, L.F.M. & Li, D., 2004. "Weighted Approximations of Tail Copula Processes with Application to Testing the Multivariate Extreme Value Condition," Discussion Paper 2004-71, Tilburg University, Center for Economic Research.
    7. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    8. Wang, Xing & Peng, Liang, 2016. "Inference for intermediate Haezendonck–Goovaerts risk measure," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 231-240.
    9. Geluk, J. & de Haan, L. & Resnick, S. & Starica, C., 1997. "Second-order regular variation, convolution and the central limit theorem," Stochastic Processes and their Applications, Elsevier, vol. 69(2), pages 139-159, September.

  44. Einmahl, J. H.J. & Mason, D.M., 1992. "Generalized quantile processes," Other publications TiSEM b2a76bac-045d-457f-869f-d, Tilburg University, School of Economics and Management.

    Cited by:

    1. Wang, Jin, 2019. "Asymptotics of generalized depth-based spread processes and applications," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 363-380.
    2. Einmahl, J.H.J. & Gantner, M. & Sawitzki, G., 2008. "The Shorth Plot," Other publications TiSEM 10b5cfb5-c502-46dc-8e51-5, Tilburg University, School of Economics and Management.
    3. Gantner, M., 2010. "Some nonparametric diagnostic statistical procedures and their asymptotic behavior," Other publications TiSEM eb04bdba-bf8a-4f6c-8dd8-9, Tilburg University, School of Economics and Management.
    4. Croux, Christophe & Haesbroeck, Gentiane, 1997. "An easy way to increase the finite-sample efficiency of the resampled minimum volume ellipsoid estimator," Computational Statistics & Data Analysis, Elsevier, vol. 25(2), pages 125-141, July.
    5. Baíllo, Amparo, 2003. "Total error in a plug-in estimator of level sets," DES - Working Papers. Statistics and Econometrics. WS ws032806, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Justel, Ana & Peña, Daniel & Zamar, Rubén, 1997. "A multivariate Kolmogorov-Smirnov test of goodness of fit," Statistics & Probability Letters, Elsevier, vol. 35(3), pages 251-259, October.
    7. Mulubrhan G. Haile & Lingling Zhang & David J. Olive, 2024. "Predicting Random Walks and a Data-Splitting Prediction Region," Stats, MDPI, vol. 7(1), pages 1-11, January.
    8. Jan Bierlant & Sven Buitendag & Eustasio Del Barrio & Marc Hallin, 2019. "Center-Outward Quantiles And The Measurement Of Multivariate Risk," Working Papers ECARES 2019-30, ULB -- Universite Libre de Bruxelles.
    9. J. Beirlant & A. Berlinet & G. Biau, 2008. "Higher order estimation at Lebesgue points," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(3), pages 651-677, September.
    10. Serfling, Robert, 2002. "Generalized Quantile Processes Based on Multivariate Depth Functions, with Applications in Nonparametric Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 232-247, October.
    11. Ryan Janicki & Tucker S. McElroy, 2016. "Hermite expansion and estimation of monotonic transformations of Gaussian data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 207-234, March.
    12. Barme-Delcroix, Marie-Francoise & Gather, Ursula, 2007. "Limit laws for multidimensional extremes," Statistics & Probability Letters, Elsevier, vol. 77(18), pages 1750-1755, December.
    13. Nadja Klein & Thomas Kneib, 2020. "Directional bivariate quantiles: a robust approach based on the cumulative distribution function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 225-260, June.
    14. Wang, Jin & Serfling, Robert, 2006. "Influence functions for a general class of depth-based generalized quantile functions," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 810-826, April.
    15. Beirlant, J. & Mason, D. M. & Vynckier, C., 1999. "Goodness-of-fit analysis for multivariate normality based on generalized quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 119-142, April.
    16. Alexander Zaigraev & Magdalena Alama-Bućko, 2018. "Optimal choice of order statistics under confidence region estimation in case of large samples," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(3), pages 283-305, April.
    17. Alexander Zaigraev & Magdalena Alama-Bućko, 2013. "On optimal choice of order statistics in large samples for the construction of confidence regions for the location and scale," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(4), pages 577-593, May.
    18. Di Bucchianico, A. & Einmahl, J.H.J. & Mushkudiani, N.A., 2001. "Smallest nonparametric tolerance regions," Other publications TiSEM 436f9be2-d0ad-49af-b6df-9, Tilburg University, School of Economics and Management.
    19. Polonik, Wolfgang, 1997. "Minimum volume sets and generalized quantile processes," Stochastic Processes and their Applications, Elsevier, vol. 69(1), pages 1-24, July.
    20. Vladimir I. Koltchinskii, 1998. "Differentiability of Inverse Operators and Limit Theorems for Inverse Functions," Journal of Theoretical Probability, Springer, vol. 11(3), pages 645-699, July.
    21. Baíllo, Amparo, 2003. "Total error in a plug-in estimator of level sets," Statistics & Probability Letters, Elsevier, vol. 65(4), pages 411-417, December.
    22. Polonik, Wolfgang & Yao, Qiwei, 2002. "Set-Indexed Conditional Empirical and Quantile Processes Based on Dependent Data," Journal of Multivariate Analysis, Elsevier, vol. 80(2), pages 234-255, February.

  45. Einmahl, J.H.J. & Koning, A.J., 1992. "Limit theorems for a general weighted process under random censoring," Other publications TiSEM ab26769f-cec3-4b07-9e8a-9, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, J.H.J. & Deheuvels, P., 2000. "Functional limit laws for the increments of Kaplan-Meier product-limit processes and applications," Other publications TiSEM ac9bbdc0-62f8-4b48-9a84-1, Tilburg University, School of Economics and Management.

  46. Einmahl, J.H.J. & van Zuijlen, M.C.A., 1992. "Glivenko-Cantelli-type theorems for weighted empirical distribution functions based on uniform spacings," Other publications TiSEM 72b2e44b-acbc-47d1-90e2-9, Tilburg University, School of Economics and Management.

    Cited by:

    1. Nelly Litvak & Maria Vlasiou, 2010. "A survey on performance analysis of warehouse carousel systems," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(4), pages 401-447, November.

  47. Einmahl, J. H.J., 1992. "The almost sure behavior of the weighted empirical process and the LIL for the weighted tail empirical process," Other publications TiSEM 5520438c-0aea-424b-b2c4-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.
    2. Einmahl, J.H.J. & Khmaladze, E.V., 2007. "Central Limit Theorems For Local Emprical Processes Near Boundaries of Sets," Other publications TiSEM c4c26f2d-99d3-473f-9900-e, Tilburg University, School of Economics and Management.
    3. Varron, Davit, 2011. "Some new almost sure results on the functional increments of the uniform empirical process," Stochastic Processes and their Applications, Elsevier, vol. 121(2), pages 337-356, February.

  48. Einmahl, J.H.J. & Deheuvels, P., 1992. "Approximations and two-sample tests based on P-P and Q-Q plots of the Kaplan-Meier estimators of lifetime distributions," Other publications TiSEM e277ba55-6fd6-41a1-a9fb-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Mushkudiani, N.A. & Einmahl, J.H.J., 2004. "Generalized Probability-Probability Plots," Other publications TiSEM 1f85f7ad-3af4-4af2-aa8b-2, Tilburg University, School of Economics and Management.
    2. Einmahl, J.H.J. & McKeague, I.W., 1999. "Confidence tubes for multiple quantile plots via empirical likelihood," Other publications TiSEM b64493f8-1c01-40fd-b16d-7, Tilburg University, School of Economics and Management.

  49. Einmahl, J.H.J., 1990. "The empirical distribution function as a tail estimator," Other publications TiSEM 08014dbd-2d84-43e5-ad47-7, Tilburg University, School of Economics and Management.

    Cited by:

    1. Di Bernardino, Elena & Maume-Deschamps, Véronique & Prieur, Clémentine, 2013. "Estimating a bivariate tail: A copula based approach," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 81-100.
    2. Remigijus Leipus & Anne Philippe & Vytautė Pilipauskaitė & Donatas Surgailis, 2020. "Estimating Long Memory in Panel Random‐Coefficient AR(1) Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(4), pages 520-535, July.
    3. Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-081, New York University, Leonard N. Stern School of Business-.
    4. Rootzén, Holger, 2009. "Weak convergence of the tail empirical process for dependent sequences," Stochastic Processes and their Applications, Elsevier, vol. 119(2), pages 468-490, February.
    5. Geluk, J. & de Haan, L. & Resnick, S. & Starica, C., 1997. "Second-order regular variation, convolution and the central limit theorem," Stochastic Processes and their Applications, Elsevier, vol. 69(2), pages 139-159, September.
    6. Abdelaati Daouia & Laurent Gardes & Stéphane Girard & Alexandre Lekina, 2011. "Kernel estimators of extreme level curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 311-333, August.

  50. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Christian Schluter & Mark Trede, 2019. "Size distributions reconsidered," Post-Print hal-01994353, HAL.
    2. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    3. He, Y. & Einmahl, J.H.J., 2014. "Estimation of Extreme Depth-Based Quantile Regions," Other publications TiSEM d6529c8a-8865-4c03-a064-a, Tilburg University, School of Economics and Management.
    4. El Arrouchi Mohamed & Imlahi Abdelouahid, 2005. "Optimal choice of kn-records in the extreme value index estimation," Statistics & Risk Modeling, De Gruyter, vol. 23(2/2005), pages 101-115, February.
    5. Patrice Bertail & Dimitris Politis & Haeffke Christian & Halbert White, 2004. "Subsampling the distribution of diverging statistics with applications to finance," Post-Print hal-03148840, HAL.
    6. Tsourti, Zoi & Panaretos, John, 2004. "Extreme Value Analysis of Teletraffic Data," MPRA Paper 6391, University Library of Munich, Germany.
    7. de Valk, Cees & Cai, Juan-Juan, 2018. "A high quantile estimator based on the log-generalized Weibull tail limit," Econometrics and Statistics, Elsevier, vol. 6(C), pages 107-128.
    8. Ana-Maria Gavril, 2009. "Exchange Rate Risk: Heads or Tails," Advances in Economic and Financial Research - DOFIN Working Paper Series 35, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    9. Zuoxiang, Peng & Miaomiao, Liu & Nadarajah, Saralees, 2010. "Asymptotic expansions for the location invariant moment-type estimator," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 982-998.
    10. Jozef Barunik & Lukas Vacha, 2012. "Monte Carlo-based tail exponent estimator," Papers 1201.4781, arXiv.org.
    11. Brito, Margarida & Freitas, Ana Cristina Moreira, 2008. "Edgeworth expansion for an estimator of the adjustment coefficient," Insurance: Mathematics and Economics, Elsevier, vol. 43(2), pages 203-208, October.
    12. Einmahl, John H.J. & de Haan, Laurens & Sinha, Ashoke Kumar, 1997. "Estimating the spectral measure of an extreme value distribution," Stochastic Processes and their Applications, Elsevier, vol. 70(2), pages 143-171, October.
    13. Einmahl, J.H.J. & Lin, T., 2006. "Asymptotic normality of extreme value estimators on C[0,1]," Other publications TiSEM 42acb0aa-ff83-4499-8f20-d, Tilburg University, School of Economics and Management.
    14. Goix, Nicolas & Sabourin, Anne & Clémençon, Stephan, 2017. "Sparse representation of multivariate extremes with applications to anomaly detection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 12-31.
    15. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.
    16. Patrick de Fontnouvelle & Eric Rosengren & John Jordan, 2007. "Implications of Alternative Operational Risk Modeling Techniques," NBER Chapters, in: The Risks of Financial Institutions, pages 475-505, National Bureau of Economic Research, Inc.
    17. Drees, Holger & Kaufmann, Edgar, 1998. "Selecting the optimal sample fraction in univariate extreme value estimation," Stochastic Processes and their Applications, Elsevier, vol. 75(2), pages 149-172, July.
    18. M. Gomes & Fernanda Figueiredo, 2006. "Bias reduction in risk modelling: Semi-parametric quantile estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(2), pages 375-396, September.
    19. Fátima Brilhante, M. & Ivette Gomes, M. & Pestana, Dinis, 2013. "A simple generalisation of the Hill estimator," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 518-535.
    20. David Anthoff & Richard S. J. Tol, 2021. "Testing the Dismal Theorem," CESifo Working Paper Series 8939, CESifo.
    21. Ferreira, Ana & de Haan, Laurens & Zhou, Chen, 2012. "Exceedance probability of the integral of a stochastic process," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 241-257.
    22. Christophe Dutang & Yuri Goegebeur & Armelle Guillou, 2016. "Robust and Bias-Corrected Estimation of the Probability of Extreme Failure Sets," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(1), pages 52-86, February.
    23. Jürg Hüsler & Deyuan Li, 2008. "Weak Convergence of the Empirical Mean Excess Process with Application to Estimate the Negative Tail Index," Methodology and Computing in Applied Probability, Springer, vol. 10(4), pages 577-593, December.
    24. Silvia Caserta & Casper G. de Vries, 2005. "Auctions with Numerous Bidders," Tinbergen Institute Discussion Papers 05-031/2, Tinbergen Institute.
    25. Einmahl, J.H.J. & Smeets, S.G.W.R., 2009. "Ultimate 100m World Records Through Extreme-Value Theory," Discussion Paper 2009-57, Tilburg University, Center for Economic Research.
    26. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Discussion Paper 2022-017, Tilburg University, Center for Economic Research.
    27. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M., 2011. "Estimation of extreme risk regions under multivariate regular variation," Other publications TiSEM b7a72a8d-f9bc-4129-ae9b-a, Tilburg University, School of Economics and Management.
    28. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    29. Kim, Joseph H.T. & Kim, Joocheol, 2015. "A parametric alternative to the Hill estimator for heavy-tailed distributions," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 60-71.
    30. Dufour, Jean-Marie & Kurz-Kim, Jeong-Ryeol, 2003. "Exact tests and confidence sets for the tail coefficient of a-stable distributions," Discussion Paper Series 1: Economic Studies 2003,16, Deutsche Bundesbank.
    31. Małgorzata Just & Krzysztof Echaust, 2021. "An Optimal Tail Selection in Risk Measurement," Risks, MDPI, vol. 9(4), pages 1-16, April.
    32. Fedotenkov, Igor, 2018. "A review of more than one hundred Pareto-tail index estimators," MPRA Paper 90072, University Library of Munich, Germany.
    33. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    34. Christian Schluter, 2021. "On Zipf’s law and the bias of Zipf regressions," Empirical Economics, Springer, vol. 61(2), pages 529-548, August.
    35. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2017. "Extreme M-quantiles as risk measures: From L1 to Lp optimization," TSE Working Papers 17-841, Toulouse School of Economics (TSE).
    36. Georg Mainik & Ludger Rüschendorf, 2010. "On optimal portfolio diversification with respect to extreme risks," Finance and Stochastics, Springer, vol. 14(4), pages 593-623, December.
    37. Xue-Zhong He & Youwei Li, 2015. "The Adaptiveness in Stock Markets: Testing the Stylized Facts in the Dax 30," Research Paper Series 364, Quantitative Finance Research Centre, University of Technology, Sydney.
    38. Christian Schluter, 2018. "Top Incomes, Heavy Tails, and Rank-Size Regressions," Post-Print hal-01978497, HAL.
    39. Neves, Cláudia & Pereira, António, 2010. "Detecting finiteness in the right endpoint of light-tailed distributions," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 437-444, March.
    40. Matheus Henrique Junqueira Saldanha & Adriano Kamimura Suzuki, 2023. "On dealing with the unknown population minimum in parametric inference," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(3), pages 509-535, September.
    41. Einmahl, J.H.J. & Li, Jun & Liu, Regina, 2015. "Bridging Centrality and Extremity : Refining Empirical Data Depth using Extreme Value Statistics," Other publications TiSEM bcd9783a-e07e-4da2-bc47-b, Tilburg University, School of Economics and Management.
    42. Moosup Kim & Sangyeol Lee, 2011. "Change point test for tail index for dependent data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(3), pages 297-311, November.
    43. Caers, Jef & Beirlant, Jan & Vynckier, Petra, 1998. "Bootstrap confidence intervals for tail indices," Computational Statistics & Data Analysis, Elsevier, vol. 26(3), pages 259-277, January.
    44. Zhou, Chen, 2009. "Existence and consistency of the maximum likelihood estimator for the extreme value index," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 794-815, April.
    45. Ameraoui, Abdelkader & Boukhetala, Kamal & Dupuy, Jean-François, 2016. "Bayesian estimation of the tail index of a heavy tailed distribution under random censoring," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 148-168.
    46. Daouia, Abdelaati & Florens, Jean-Pierre & Simar, Léopold, 2009. "Frontier Estimation and Extreme Values Theory," IDEI Working Papers 611, Institut d'Économie Industrielle (IDEI), Toulouse.
    47. Einmahl, John & He, Y., 2022. "Extreme Value Inference for General Heterogeneous Data," Other publications TiSEM fd8dd91c-086f-40e6-ac29-3, Tilburg University, School of Economics and Management.
    48. Beirlant, J. & Kijko, Andrzej & Reykens, Tom & Einmahl, John, 2017. "Estimating the Maximum Possible Earthquake Magnitude Using Extreme Value Methodology : the Groningen Case," Other publications TiSEM 65e5595c-7ec1-4723-bf0e-8, Tilburg University, School of Economics and Management.
    49. A. Dematteo & S. Clémençon, 2016. "On tail index estimation based on multivariate data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(1), pages 152-176, March.
    50. Einmahl, J.H.J. & Li, J. & Liu, R.Y., 2006. "Extreme Value Theory Approach to Simultaneous Monitoring and Thresholding of Multiple Risk Indicators," Other publications TiSEM 4e0aab6a-b885-4a21-a898-2, Tilburg University, School of Economics and Management.
    51. Enrico Biffis & Erik Chavez, 2014. "Tail Risk in Commercial Property Insurance," Risks, MDPI, vol. 2(4), pages 1-18, September.
    52. Ahmed, Hanan, 2022. "Extreme value statistics using related variables," Other publications TiSEM 246f0f13-701c-4c0d-8e09-e, Tilburg University, School of Economics and Management.
    53. Minhee Kim & Todd Allen & Kaibo Liu, 2023. "Covariate Dependent Sparse Functional Data Analysis," INFORMS Joural on Data Science, INFORMS, vol. 2(1), pages 81-98, April.
    54. Chao Huang & Jin-Guan Lin, 2014. "Modified maximum spacings method for generalized extreme value distribution and applications in real data analysis," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 867-894, October.
    55. Beirlant, J. & Vandewalle, B., 2002. "Some comments on the estimation of a dependence index in bivariate extreme value statistics," Statistics & Probability Letters, Elsevier, vol. 60(3), pages 265-278, December.
    56. Ana Ferreira & Casper G. de Vries, 2004. "Optimal Confidence Intervals for the Tail Index and High Quantiles," Tinbergen Institute Discussion Papers 04-090/2, Tinbergen Institute.
    57. Wai Leong Ng & Chun Yip Yau, 2018. "Test for the existence of finite moments via bootstrap," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 28-48, January.
    58. Marco Rocco, 2011. "Extreme value theory for finance: a survey," Questioni di Economia e Finanza (Occasional Papers) 99, Bank of Italy, Economic Research and International Relations Area.
    59. Christophe Dutang & Yuri Goegebeur & Armelle Guillou, 2016. "Robust and bias-corrected estimation of the probability of extreme failure sets," Post-Print hal-01616187, HAL.
    60. Deyuan Li & Liang Peng & Yongcheng Qi, 2011. "Empirical likelihood confidence intervals for the endpoint of a distribution function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 353-366, August.
    61. Zakaria Babutsidze, 2016. "Innovation, competition and firm size distribution on fragmented markets," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 143-169, March.
    62. Krzysztof Echaust & Małgorzata Just, 2020. "Value at Risk Estimation Using the GARCH-EVT Approach with Optimal Tail Selection," Mathematics, MDPI, vol. 8(1), pages 1-24, January.
    63. Geluk, J. L. & Peng, Liang, 2000. "An adaptive optimal estimate of the tail index for MA(l) time series," Statistics & Probability Letters, Elsevier, vol. 46(3), pages 217-227, February.
    64. de Haan, Laurens & Canto e Castro, Luisa, 2006. "A class of distribution functions with less bias in extreme value estimation," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1617-1624, September.
    65. Gamermann, D. & Triana-Dopico, J. & Jaime, R., 2019. "A comprehensive statistical study of metabolic and protein–protein interaction network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    66. V. A. Pavlenko, 2017. "Estimation of the upper bound of seismic hazard curve by using the generalised extreme value distribution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 19-33, October.
    67. Einmahl, J.H.J. & Magnus, J.R., 2006. "Records in Athletics through Extreme-Value Theory," Discussion Paper 2006-83, Tilburg University, Center for Economic Research.
    68. Tobbal, Khelifa, 1996. "A functional law of the iterated logarithm for the Dekkers-Einmahl-de Haan tail index estimator," Statistics & Probability Letters, Elsevier, vol. 29(1), pages 15-22, August.
    69. Ivanilda Cabral & Frederico Caeiro & M. Ivette Gomes, 2022. "On the comparison of several classical estimators of the extreme value index," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 51(1), pages 179-196, January.
    70. Einmahl, J.H.J. & Fils-Villetard, A. & Guillou, A., 2006. "Statistics of Extremes under Random Censoring," Other publications TiSEM 62d47475-e6e9-43d6-9461-5, Tilburg University, School of Economics and Management.
    71. M. Alves, 2001. "Weiss-Hill estimator," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 10(1), pages 203-224, June.
    72. Einmahl, J.H.J. & Khmaladze, E.V., 2007. "Central Limit Theorems For Local Emprical Processes Near Boundaries of Sets," Other publications TiSEM c4c26f2d-99d3-473f-9900-e, Tilburg University, School of Economics and Management.
    73. Vygantas Paulauskas & Marijus Vaičiulis, 2017. "A class of new tail index estimators," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 461-487, April.
    74. Neves, Claudia & Fraga Alves, M. I., 2004. "Reiss and Thomas' automatic selection of the number of extremes," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 689-704, November.
    75. Ghosh, Souvik & Resnick, Sidney, 2010. "A discussion on mean excess plots," Stochastic Processes and their Applications, Elsevier, vol. 120(8), pages 1492-1517, August.
    76. Iglesias, Emma M. & Linton, Oliver, 2009. "Estimation of tail thickness parameters from GJR-GARCH models," UC3M Working papers. Economics we094726, Universidad Carlos III de Madrid. Departamento de Economía.
    77. Rishikesh Yadav & Raphaël Huser & Thomas Opitz, 2021. "Spatial hierarchical modeling of threshold exceedances using rate mixtures," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    78. Brito, Margarida & Freitas, Ana Cristina Moreira, 2010. "Consistent estimation of the tail index for dependent data," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1835-1843, December.
    79. Sun, Haoze & Jiang, Yuexiang, 2014. "Empirical likelihood based confidence intervals for the tail index when γ<−1/2," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 149-157.
    80. Gomes, M. Ivette & Neves, Cláudia, 2008. "Asymptotic comparison of the mixed moment and classical extreme value index estimators," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 643-653, April.
    81. Charnchai Leuwattanachotinan & Casper G. de Vries, 2015. "Extreme Linkages in Financial Markets: Macro Shocks and Systemic Risk," PIER Discussion Papers 2, Puey Ungphakorn Institute for Economic Research.
    82. Caers, Jef & Dyck, Jozef Van, 1998. "Nonparametric tail estimation using a double bootstrap method," Computational Statistics & Data Analysis, Elsevier, vol. 29(2), pages 191-211, December.
    83. Gilles Stupfler & Fan Yang, 2018. "Analyzing and Predicting CAT Bond Premiums: a Financial Loss Premium Principle and Extreme Value Modeling," Post-Print hal-04464416, HAL.
    84. Chao Huang & Jin-Guan Lin & Yan-Yan Ren, 2012. "Statistical Inferences for Generalized Pareto Distribution Based on Interior Penalty Function Algorithm and Bootstrap Methods and Applications in Analyzing Stock Data," Computational Economics, Springer;Society for Computational Economics, vol. 39(2), pages 173-193, February.
    85. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    86. Beirlant, J. & Bouquiaux, C. & Werker, B.J.M., 2006. "Semiparametric lower bounds for tail-index estimation," Other publications TiSEM 4f434455-72a7-4b68-b972-d, Tilburg University, School of Economics and Management.
    87. Einmahl, J.H.J. & de Haan, L.F.M. & Krajina, A., 2009. "Estimating Extreme Bivariate Quantile Regions," Other publications TiSEM 007ce0a9-dd94-4301-ad62-1, Tilburg University, School of Economics and Management.
    88. Matias Heikkilä & Yves Dominicy & Pauliina Ilmonen, 2017. "Multivariate moment based extreme value index estimators," Computational Statistics, Springer, vol. 32(4), pages 1481-1513, December.
    89. Drees, Holger & Huang, Xin, 1998. "Best Attainable Rates of Convergence for Estimators of the Stable Tail Dependence Function," Journal of Multivariate Analysis, Elsevier, vol. 64(1), pages 25-47, January.
    90. Mohamed El Ghourabi & Amira Dridi & Mohamed Limam, 2015. "A new financial stress index model based on support vector regression and control chart," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 775-788, April.
    91. Stupfler, Gilles, 2016. "Estimating the conditional extreme-value index under random right-censoring," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 1-24.
    92. Goedele Dierckx & Yuri Goegebeur & Armelle Guillou, 2021. "Local Robust Estimation of Pareto-Type Tails with Random Right Censoring," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 70-108, February.
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    100. Zhou, Chen, 2010. "The extent of the maximum likelihood estimator for the extreme value index," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 971-983, April.
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    102. Albert, Clément & Dutfoy, Anne & Gardes, Laurent & Girard, Stéphane, 2020. "An extreme quantile estimator for the log-generalized Weibull-tail model," Econometrics and Statistics, Elsevier, vol. 13(C), pages 137-174.
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    106. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.
    107. Dias, Alexandra, 2014. "Semiparametric estimation of multi-asset portfolio tail risk," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 398-408.
    108. Mohamed El Ghourabi & Asma Nani & Imed Gammoudi, 2021. "A value‐at‐risk computation based on heavy‐tailed distribution for dynamic conditional score models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2790-2799, April.
    109. Zhu, Sha & Dekker, Rommert & van Jaarsveld, Willem & Renjie, Rex Wang & Koning, Alex J., 2017. "An improved method for forecasting spare parts demand using extreme value theory," European Journal of Operational Research, Elsevier, vol. 261(1), pages 169-181.
    110. Laurent Gardes & Stéphane Girard, 2021. "On the estimation of the variability in the distribution tail," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 884-907, December.
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    113. Hüsler, Jürg & Li, Deyuan & Müller, Samuel, 2006. "Weighted least squares estimation of the extreme value index," Statistics & Probability Letters, Elsevier, vol. 76(9), pages 920-930, May.
    114. Stéphane Girard & Armelle Guillou & Gilles Stupfler, 2012. "Estimating an endpoint with high-order moments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(4), pages 697-729, December.
    115. Daouia, Abdelaati & Padoan, Simone A. & Stupfler, Gilles, 2023. "Extreme expectile estimation for short-tailed data, with an application to market risk assessment," TSE Working Papers 23-1414, Toulouse School of Economics (TSE).
    116. Wager, Stefan, 2014. "Subsampling extremes: From block maxima to smooth tail estimation," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 335-353.
    117. H. M. Barakat & E. M. Nigm & O. M. Khaled & H. A. Alaswed, 2018. "The estimations under power normalization for the tail index, with comparison," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 431-454, July.
    118. Himadri Ghosh & Prajneshu, 2011. "Statistical learning theory for fitting multimodal distribution to rainfall data: an application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2533-2545, January.
    119. Stéphane Girard & Gilles Claude Stupfler & Antoine Usseglio-Carleve, 2021. "Extreme Conditional Expectile Estimation in Heavy-Tailed Heteroscedastic Regression Models," Post-Print hal-03306230, HAL.
    120. Krajina, A., 2010. "An M-estimator of multivariate tail dependence," Other publications TiSEM 66518e07-db9a-4446-81be-c, Tilburg University, School of Economics and Management.
    121. Tsourti, Zoi & Panaretos, John, 2003. "Extreme Value Index Estimators and Smoothing Alternatives: A Critical Review," MPRA Paper 6390, University Library of Munich, Germany.
    122. Waldemar Tarczyński & Sebastian Majewski & Małgorzata Tarczyńska-Łuniewska & Agnieszka Majewska & Grzegorz Mentel, 2021. "The Impact of Weather Factors on Quotations of Energy Sector Companies on Warsaw Stock Exchange," Energies, MDPI, vol. 14(6), pages 1-14, March.
    123. Evandro Konzen & Cláudia Neves & Philip Jonathan, 2021. "Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference," Environmetrics, John Wiley & Sons, Ltd., vol. 32(4), June.
    124. Fedotenkov, Igor, 2015. "A note on the bootstrap method for testing the existence of finite moments," MPRA Paper 66033, University Library of Munich, Germany.
    125. Estate Khmaladze & Wolfgang Weil, 2008. "Local empirical processes near boundaries of convex bodies," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 813-842, December.
    126. Geluk, J. L., 1996. "On the domain of attraction of exp(-exp(-x))," Statistics & Probability Letters, Elsevier, vol. 31(2), pages 91-95, December.
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    128. Li, Youwei & Hamill, Philip A. & Opong, Kwaku K., 2010. "Do benchmark African equity indices exhibit the stylized facts?," Global Finance Journal, Elsevier, vol. 21(1), pages 71-97.
    129. McElroy, Tucker & Jach, Agnieszka, 2012. "Tail index estimation in the presence of long-memory dynamics," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 266-282.
    130. Fedotenkov, Igor, 2015. "A simple nonparametric test for the existence of finite moments," MPRA Paper 66089, University Library of Munich, Germany.
    131. Martins, Ana Paula & Ferreira, Helena & Ferreira, Marta, 2022. "A new random field on lattices," Statistics & Probability Letters, Elsevier, vol. 186(C).
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    133. Beirlant, Jan & Goegebeur, Yuri, 2003. "Regression with response distributions of Pareto-type," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 595-619, April.
    134. Allen, Michael R. & Datta, Somnath, 1999. "Estimation of the index parameter for autoregressive data using the estimated innovations," Statistics & Probability Letters, Elsevier, vol. 41(3), pages 315-324, February.
    135. Li, Zhouping & Gong, Yun & Peng, Liang, 2010. "Empirical likelihood method for intermediate quantiles," Statistics & Probability Letters, Elsevier, vol. 80(11-12), pages 1022-1029, June.
    136. Natalia Markovich & Marijus Vaičiulis, 2023. "Extreme Value Statistics for Evolving Random Networks," Mathematics, MDPI, vol. 11(9), pages 1-35, May.
    137. Emanuele Taufer & Flavio Santi & Pier Luigi Novi Inverardi & Giuseppe Espa & Maria Michela Dickson, 2020. "Extreme Value Index Estimation by Means of an Inequality Curve," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
    138. Shaul Bar-Lev, 2008. "Point and confidence interval estimates for a global maximum via extreme value theory," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(12), pages 1371-1381.
    139. Daouia, Abdelaati & Laurent, Thibault & Noh, Hohsuk, 2017. "npbr: A Package for Nonparametric Boundary Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i09).
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    141. Necir, Abdelhakim & Meraghni, Djamel, 2009. "Empirical estimation of the proportional hazard premium for heavy-tailed claim amounts," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 49-58, August.
    142. Hsieh, Ping-Hung, 2002. "An exploratory first step in teletraffic data modeling: evaluation of long-run performance of parameter estimators," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 263-283, August.
    143. Laurens F.M. de Haan & Liang Peng & T.T. Pereira, 1997. "A Bootstrap-based Method to Achieve Optimality in Estimating the Extreme-value Index," Tinbergen Institute Discussion Papers 97-099/4, Tinbergen Institute.
    144. Ferreira, Helena & Ferreira, Marta, 2015. "Extremes of scale mixtures of multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 82-99.
    145. Cui, Hengxin & Tan, Ken Seng & Yang, Fan & Zhou, Chen, 2022. "Asymptotic analysis of portfolio diversification," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 302-325.
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    148. Phornchanok Cumperayot & Casper G. de Vries, 2006. "Large Swings in Currencies driven by Fundamentals," Tinbergen Institute Discussion Papers 06-086/2, Tinbergen Institute.

  51. Einmahl, J. H. & Mason, D. M., 1988. "Strong limit theorems for weighted quantile processes," Other publications TiSEM 4bbe972d-b641-42a4-b2b8-0, Tilburg University, School of Economics and Management.

    Cited by:

    1. Leclerc J., 2000. "Strong Limiting Behavior Of Two Estimates Of The Mode : The Shorth And The Naive Estimator," Statistics & Risk Modeling, De Gruyter, vol. 18(4), pages 413-428, April.
    2. Abdelhakim Necir, 2006. "A Nonparametric Sequential Test with Power 1 for the Mean of Lévy-stable Laws with Infinite Variance," Methodology and Computing in Applied Probability, Springer, vol. 8(3), pages 321-343, September.
    3. Zhou, Chen, 2009. "Existence and consistency of the maximum likelihood estimator for the extreme value index," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 794-815, April.
    4. de Valk, Cees, 2016. "A large deviations approach to the statistics of extreme events," Other publications TiSEM 117b3ba0-0e40-4277-b25e-d, Tilburg University, School of Economics and Management.
    5. Hüsler, Jürg & Li, Deyuan & Müller, Samuel, 2006. "Weighted least squares estimation of the extreme value index," Statistics & Probability Letters, Elsevier, vol. 76(9), pages 920-930, May.
    6. Fabian Dunker & Stephan Klasen & Tatyana Krivobokova, 2017. "Asymptotic Distribution and Simultaneous Confidence Bands for Ratios of Quantile Functions," Papers 1710.09009, arXiv.org.

  52. Einmahl, J.H.J. & Deheuvels, P. & Mason, D.M. & Ruymgaart, F.H., 1988. "The almost sure behavior of maximal and minimal multivariate k_n -spacings," Other publications TiSEM 8867b285-2eab-4ec2-aec0-5, Tilburg University, School of Economics and Management.

    Cited by:

    1. Claire Coiffard, 2011. "On the Hausdorff dimension of exceptional random sets generated by multivariate spacings," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(3), pages 359-371, May.
    2. Major, Péter, 2016. "Sharp tail distribution estimates for the supremum of a class of sums of i.i.d. random variables," Stochastic Processes and their Applications, Elsevier, vol. 126(1), pages 118-137.
    3. Carando, Daniel & Fraiman, Ricardo & Groisman, Pablo, 2009. "Nonparametric likelihood based estimation for a multivariate Lipschitz density," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 981-992, May.

  53. Einmahl, J.H.J. & van Zuijlen, M.C.A., 1988. "Strong bounds for weighted empirical distribution functions based on uniform spacings," Other publications TiSEM 21059503-7dec-4f2c-a621-6, Tilburg University, School of Economics and Management.

    Cited by:

    1. Nelly Litvak & Maria Vlasiou, 2010. "A survey on performance analysis of warehouse carousel systems," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(4), pages 401-447, November.

  54. Einmahl, J.H.J. & Mason, D.M., 1988. "Laws of the iterated logarithm in the tails for weighted uniform empirical processes," Other publications TiSEM d5a5a8d5-c060-4344-9675-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.
    2. A. Guillou & P. Naveau & J. Diebolt & P. Ribereau, 2009. "Return level bounds for discrete and continuous random variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 584-604, November.

  55. Einmahl, J.H.J. & Haeusler, E. & Mason, D.M., 1988. "On the relationship between the almost sure stability of weighted empirical distributions and sums of order statistics," Other publications TiSEM df0f63ff-d20e-4578-86ae-8, Tilburg University, School of Economics and Management.

    Cited by:

    1. Csörgo, Sándor & Simons, Gordon, 1996. "A strong law of large numbers for trimmed sums, with applications to generalized St. Petersburg games," Statistics & Probability Letters, Elsevier, vol. 26(1), pages 65-73, January.
    2. Marc Kesseböhmer & Tanja Schindler, 2019. "Strong Laws of Large Numbers for Intermediately Trimmed Sums of i.i.d. Random Variables with Infinite Mean," Journal of Theoretical Probability, Springer, vol. 32(2), pages 702-720, June.

  56. Einmahl, J.H.J., 1987. "Multivariate empirical processes," Other publications TiSEM 4d74fa6b-5281-48ea-aa4d-5, Tilburg University, School of Economics and Management.

    Cited by:

    1. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    2. Einmahl, John & Yang, Fan & Zhou, Chen, 2018. "Testing the Multivariate Regular Variation Model," Discussion Paper 2018-044, Tilburg University, Center for Economic Research.
    3. Gantner, M., 2010. "Some nonparametric diagnostic statistical procedures and their asymptotic behavior," Other publications TiSEM eb04bdba-bf8a-4f6c-8dd8-9, Tilburg University, School of Economics and Management.
    4. Einmahl, J.H.J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Other publications TiSEM 07d934b9-2bd4-474a-bf32-f, Tilburg University, School of Economics and Management.
    5. Juan-Juan Cai & John H. J. Einmahl & Laurens Haan & Chen Zhou, 2015. "Estimation of the marginal expected shortfall: the mean when a related variable is extreme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 417-442, March.
    6. Einmahl, J.H.J. & Gantner, M., 2012. "Testing for bivariate spherical symmetry," Other publications TiSEM f02b446f-b69b-45bb-b39d-2, Tilburg University, School of Economics and Management.
    7. Einmahl, J.H.J. & McKeague, I.W., 2003. "Empirical likelihood based hypothesis testing," Other publications TiSEM 2ddb34d8-8ae7-46e3-8004-c, Tilburg University, School of Economics and Management.
    8. Billio Monica & Frattarolo Lorenzo & Guégan Dominique, 2021. "Multivariate radial symmetry of copula functions: finite sample comparison in the i.i.d case," Dependence Modeling, De Gruyter, vol. 9(1), pages 43-61, January.
    9. John H. J. Einmahl & Andrew Rosalsky, 2001. "The Functional Law of the Iterated Logarithm for the Empirical Process Based on Sample Means," Journal of Theoretical Probability, Springer, vol. 14(2), pages 577-597, April.
    10. B. N. Cheng & S. T. Rachev, 1995. "Multivariate Stable Futures Prices," Mathematical Finance, Wiley Blackwell, vol. 5(2), pages 133-153, April.

  57. Einmahl, J.H.J. & Ruymgaart, F.H., 1987. "The almost sure behaviour of the oscillation modulus of the multivariate empirical process," Other publications TiSEM 94429b0f-0175-452f-b41b-f, Tilburg University, School of Economics and Management.

    Cited by:

    1. Peng, Liang & Qi, Yongcheng, 2008. "Bootstrap approximation of tail dependence function," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1807-1824, September.
    2. Gery Geenens & Arthur Charpentier & Davy Paindaveine, 2014. "Probit Transformation for Nonparametric Kernel Estimation of the Copula Density," Working Papers ECARES ECARES 2014-23, ULB -- Universite Libre de Bruxelles.
    3. Alexandre Leblanc, 2009. "Chung–Smirnov property for Bernstein estimators of distribution functions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(2), pages 133-142.
    4. Deheuvels, Paul & Peccati, Giovanni & Yor, Marc, 2006. "On quadratic functionals of the Brownian sheet and related processes," Stochastic Processes and their Applications, Elsevier, vol. 116(3), pages 493-538, March.
    5. John H. J. Einmahl & Andrew Rosalsky, 2001. "The Functional Law of the Iterated Logarithm for the Empirical Process Based on Sample Means," Journal of Theoretical Probability, Springer, vol. 14(2), pages 577-597, April.

  58. Einmahl, J.H.J. & Mason, D.M., 1985. "Bounds for weighted multivariate empirical distribution functions," Other publications TiSEM 4a583237-c93b-4a57-8b7c-a, Tilburg University, School of Economics and Management.

    Cited by:

    1. Zhao, Sihai Dave & Cai, T. Tony & Li, Hongzhe, 2017. "Optimal detection of weak positive latent dependence between two sequences of multiple tests," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 169-184.

Articles

  1. Jan Beirlant & Andrzej Kijko & Tom Reynkens & John H. J. Einmahl, 2019. "Estimating the maximum possible earthquake magnitude using extreme value methodology: the Groningen case," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 98(3), pages 1091-1113, September.
    See citations under working paper version above.
  2. Jesson J. Einmahl & John H. J. Einmahl & Laurens de Haan, 2019. "Limits to Human Life Span Through Extreme Value Theory," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1075-1080, July.
    See citations under working paper version above.
  3. Yi He & John H. J. Einmahl, 2017. "Estimation of extreme depth-based quantile regions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 449-461, March.
    See citations under working paper version above.
  4. John H. J. Einmahl & Laurens Haan & Chen Zhou, 2016. "Statistics of heteroscedastic extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 31-51, January.
    See citations under working paper version above.
  5. John H. J. Einmahl & Anna Kiriliouk & Andrea Krajina & Johan Segers, 2016. "An M-estimator of spatial tail dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 275-298, January.
    See citations under working paper version above.
  6. Juan-Juan Cai & John H. J. Einmahl & Laurens Haan & Chen Zhou, 2015. "Estimation of the marginal expected shortfall: the mean when a related variable is extreme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 417-442, March.
    See citations under working paper version above.
  7. John Einmahl & Maria Gantner, 2012. "Testing for bivariate spherical symmetry," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 54-73, March.
    See citations under working paper version above.
  8. John H. J. Einmahl & Sander G. W. R. Smeets, 2011. "Ultimate 100‐m world records through extreme‐value theory," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(1), pages 32-42, February.
    See citations under working paper version above.
  9. Jan Beirlant & John H. J. Einmahl, 2010. "Asymptotics for the Hirsch Index," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 355-364, September.
    See citations under working paper version above.
  10. Einmahl, John H. J. & Li, Jun & Liu, Regina Y., 2009. "Thresholding Events of Extreme in Simultaneous Monitoring of Multiple Risks," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 982-992.

    Cited by:

    1. Polanski, Arnold & Stoja, Evarist & Chiu, Ching-Wai (Jeremy), 2019. "Tail risk interdependence," Bank of England working papers 815, Bank of England.
    2. Cai, J., 2012. "Estimation concerning risk under extreme value conditions," Other publications TiSEM a92b089f-bc4c-41c2-b297-c, Tilburg University, School of Economics and Management.
    3. Goix, Nicolas & Sabourin, Anne & Clémençon, Stephan, 2017. "Sparse representation of multivariate extremes with applications to anomaly detection," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 12-31.
    4. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M., 2011. "Estimation of extreme risk regions under multivariate regular variation," Other publications TiSEM b7a72a8d-f9bc-4129-ae9b-a, Tilburg University, School of Economics and Management.
    5. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "Beyond dimension two: A test for higher-order tail risk," SFB 649 Discussion Papers SFB649DP2014-042, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Christian Francq & Jean-Michel Zakoïan, 2011. "Estimating the Marginal Law of a Time Series with Applications to Heavy Tailed Distributions," Working Papers 2011-30, Center for Research in Economics and Statistics.
    7. Einmahl, J.H.J. & de Haan, L.F.M. & Krajina, A., 2009. "Estimating Extreme Bivariate Quantile Regions," Other publications TiSEM 007ce0a9-dd94-4301-ad62-1, Tilburg University, School of Economics and Management.
    8. Arnold Polanski & Evarist Stoja & Ching‐Wai (Jeremy) Chiu, 2021. "Tail risk interdependence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5499-5511, October.
    9. Carsten Bormann & Melanie Schienle & Julia Schaumburg, 2014. "A Test for the Portion of Bivariate Dependence in Multivariate Tail Risk," Tinbergen Institute Discussion Papers 14-024/III, Tinbergen Institute, revised 23 Jun 2014.

  11. Einmahl, John H. J. & Magnus, Jan R., 2008. "Records in Athletics Through Extreme-Value Theory," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1382-1391.
    See citations under working paper version above.
  12. Einmahl, John H.J. & Van Keilegom, Ingrid, 2008. "Specification tests in nonparametric regression," Journal of Econometrics, Elsevier, vol. 143(1), pages 88-102, March.
    See citations under working paper version above.
  13. Einmahl, John H. J., 1997. "Poisson and Gaussian approximation of weighted local empirical processes," Stochastic Processes and their Applications, Elsevier, vol. 70(1), pages 31-58, October.
    See citations under working paper version above.
  14. Einmahl, John H.J. & de Haan, Laurens & Sinha, Ashoke Kumar, 1997. "Estimating the spectral measure of an extreme value distribution," Stochastic Processes and their Applications, Elsevier, vol. 70(2), pages 143-171, October.
    See citations under working paper version above.
  15. J. Beirlant & J. H. J. Einmahl, 1995. "Asymptotic confidence intervals for the length of the shortt under random censoring," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 49(1), pages 1-8, March.
    See citations under working paper version above.
  16. Einmahl, J. H. J. & Dehaan, L. & Huang, X., 1993. "Estimating a Multidimensional Extreme-Value Distribution," Journal of Multivariate Analysis, Elsevier, vol. 47(1), pages 35-47, October.
    See citations under working paper version above.
  17. Einmahl, John H. J. & van Zuijlen, Martien C. A., 1992. "Glivenko--Cantelli-type theorems for weighted empirical distribution functions based on uniform spacings," Statistics & Probability Letters, Elsevier, vol. 13(5), pages 411-419, April.
    See citations under working paper version above.
  18. Deheuvels, Paul & Einmahl, John H. J., 1992. "Approximations and two-sample tests based on P-P and Q-Q plots of the Kaplan-Meier estimators of lifetime distributions," Journal of Multivariate Analysis, Elsevier, vol. 43(2), pages 200-217, November.
    See citations under working paper version above.
  19. J.H.J. Einmahl, 1990. "The empirical distribution function as a tail estimator," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 44(2), pages 79-82, June.
    See citations under working paper version above.
  20. Deheuvels, Paul & Einmahl, John H. J. & Mason, David M. & Ruymgaart, Frits H., 1988. "The almost sure behavior of maximal and minimal multivariate kn-spacings," Journal of Multivariate Analysis, Elsevier, vol. 24(1), pages 155-176, January.
    See citations under working paper version above.
  21. Einmahl, J. H. J. & Ruymgaart, F. H., 1987. "The almost sure behavior of the oscillation modulus of the multivariate empirical process," Statistics & Probability Letters, Elsevier, vol. 6(2), pages 87-96, November.
    See citations under working paper version above.
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