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Constraints on concordance measures in bivariate discrete data

Citations

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Cited by:

  1. Fantazzini, Dean, 2020. "Discussing copulas with Sergey Aivazian: a memoir," MPRA Paper 102317, University Library of Munich, Germany.
  2. Lee, Sangyeol, 2014. "Goodness of fit test for discrete random variables," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 92-100.
  3. Heinen, Andréas & Rengifo, Erick, 2008. "Multivariate reduced rank regression in non-Gaussian contexts, using copulas," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2931-2944, February.
  4. Neslehová, Johanna, 2007. "On rank correlation measures for non-continuous random variables," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 544-567, March.
  5. Emanuela Raffinetti & Pier Alda Ferrari, 2021. "A dependence measure flow tree through Monte Carlo simulations," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 467-496, April.
  6. Ben Omrane, Walid & Heinen, Andréas, 2009. "Is there any common knowledge news in the Euro/Dollar market?," International Review of Economics & Finance, Elsevier, vol. 18(4), pages 656-670, October.
  7. Koen Decancq, 2014. "Copula-based measurement of dependence between dimensions of well-being," Oxford Economic Papers, Oxford University Press, vol. 66(3), pages 681-701.
  8. Michael Stanley Smith, 2021. "Implicit Copulas: An Overview," Papers 2109.04718, arXiv.org.
  9. Aristidis Nikoloulopoulos & Dimitris Karlis, 2010. "Regression in a copula model for bivariate count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1555-1568.
  10. Perrone, Elisa & van den Heuvel, Edwin R. & Zhan, Zhuozhao, 2023. "Kendall’s tau estimator for bivariate zero-inflated count data," Statistics & Probability Letters, Elsevier, vol. 199(C).
  11. Hughes, John, 2021. "On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins," Statistics & Probability Letters, Elsevier, vol. 177(C).
  12. Pravin Trivedi & David Zimmer, 2017. "A Note on Identification of Bivariate Copulas for Discrete Count Data," Econometrics, MDPI, vol. 5(1), pages 1-11, February.
  13. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2008. "A multivariate integer count hurdle model: theory and application to exchange rate dynamics," Studies in Empirical Economics, in: Luc Bauwens & Winfried Pohlmeier & David Veredas (ed.), High Frequency Financial Econometrics, pages 31-48, Springer.
  14. Eduardo Fé & Richard Hofler, 2013. "Count data stochastic frontier models, with an application to the patents–R&D relationship," Journal of Productivity Analysis, Springer, vol. 39(3), pages 271-284, June.
  15. Bolancé, Catalina & Vernic, Raluca, 2019. "Multivariate count data generalized linear models: Three approaches based on the Sarmanov distribution," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 89-103.
  16. Denuit, Michel M. & Mesfioui, Mhamed, 2017. "Bounds on Kendall’s tau for zero-inflated continuous variables," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 173-178.
  17. Eugenio Miravete, 2014. "Testing for complementarities among countable strategies," Empirical Economics, Springer, vol. 46(4), pages 1521-1544, June.
  18. Nagler, Thomas, 2018. "A generic approach to nonparametric function estimation with mixed data," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 326-330.
  19. Genest, Christian & Nešlehová, Johanna G. & Rémillard, Bruno, 2013. "On the estimation of Spearman’s rho and related tests of independence for possibly discontinuous multivariate data," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 214-228.
  20. José Murteira & Óscar Lourenço, 2011. "Health care utilization and self-assessed health: specification of bivariate models using copulas," Empirical Economics, Springer, vol. 41(2), pages 447-472, October.
  21. Heinen, Andreas & Rengifo, Erick, 2007. "Multivariate autoregressive modeling of time series count data using copulas," Journal of Empirical Finance, Elsevier, vol. 14(4), pages 564-583, September.
  22. Fantazzini, Dean, 2008. "Econometric Analysis of Financial Data in Risk Management (continuation). Section III: Managing Operational Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 11(3), pages 87-122.
  23. Igor Kheifets & Carlos Velasco, 2012. "Model Adequacy Checks for Discrete Choice Dynamic Models," Working Papers w0170, New Economic School (NES).
  24. Grammig, Joachim & Kehrle, Kerstin, 2008. "A new marked point process model for the federal funds rate target: Methodology and forecast evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2370-2396, July.
  25. L. L. Henn, 2022. "Limitations and performance of three approaches to Bayesian inference for Gaussian copula regression models of discrete data," Computational Statistics, Springer, vol. 37(2), pages 909-946, April.
  26. Siem Jan Koopman & Rutger Lit & André Lucas & Anne Opschoor, 2018. "Dynamic discrete copula models for high‐frequency stock price changes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(7), pages 966-985, November.
  27. Kaveh Salehzadeh Nobari, 2021. "Pair copula constructions of point-optimal sign-based tests for predictive linear and nonlinear regressions," Papers 2111.04919, arXiv.org.
  28. Mothafer, Ghasak I.M.A. & Yamamoto, Toshiyuki & Shankar, Venkataraman N., 2018. "A multivariate heterogeneous-dispersion count model for asymmetric interdependent freeway crash types," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 84-105.
  29. Xiaotian Zheng & Athanasios Kottas & Bruno Sansó, 2023. "Bayesian geostatistical modeling for discrete‐valued processes," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), November.
  30. Shi, Peng & Valdez, Emiliano A., 2014. "Multivariate negative binomial models for insurance claim counts," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 18-29.
  31. Denuit, Michel & Mesfioui, Mhamed, 2016. "Bounds on Kendall’s Tau for Zero-Inflated Continuous Variables," LIDAM Discussion Papers ISBA 2016043, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  32. Ying Zhang & Chuancun Yin, 2014. "A new multivariate dependence measure based on comonotonicity," Papers 1410.7845, arXiv.org.
  33. Lambert, Philippe, 2007. "Archimedean copula estimation using Bayesian splines smoothing techniques," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6307-6320, August.
  34. Mesfioui, Mhamed & Quessy, Jean-François, 2010. "Concordance measures for multivariate non-continuous random vectors," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2398-2410, November.
  35. Faugeras, Olivier P., 2015. "Maximal coupling of empirical copulas for discrete vectors," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 179-186.
  36. Denuit, Michel & Kiriliouk, Anna & Segers, Johan, 2015. "Max-factor individual risk models with application to credit portfolios," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 162-172.
  37. George Tzougas & Despoina Makariou, 2022. "The multivariate Poisson‐Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(4), pages 401-417, December.
  38. Denuit, Michel & Mesfoui, Mhamed & Trufin, Julien, 2019. "Concordance-based predictive measures in regression models for discrete responses," LIDAM Discussion Papers ISBA 2019005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  39. Geenens Gery, 2020. "Copula modeling for discrete random vectors," Dependence Modeling, De Gruyter, vol. 8(1), pages 417-440, January.
  40. Fokianos, Konstantinos & Fried, Roland & Kharin, Yuriy & Voloshko, Valeriy, 2022. "Statistical analysis of multivariate discrete-valued time series," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  41. Eugenio J. Miravete, 2009. "Competing with Menus of Tariff Options," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 188-205, March.
  42. Antonella D’agostino & Giovanni De Luca & Dominique Guégan, 2023. "Estimating Lower Tail Dependence Between Pairs of Poverty Dimensions in Europe," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 419-442, June.
  43. Wei, Zheng & Kim, Daeyoung, 2021. "On exploratory analytic method for multi-way contingency tables with an ordinal response variable and categorical explanatory variables," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
  44. Smith, Michael Stanley, 2023. "Implicit Copulas: An Overview," Econometrics and Statistics, Elsevier, vol. 28(C), pages 81-104.
  45. Genest, Christian & Nešlehová, Johanna G. & Rémillard, Bruno, 2017. "Asymptotic behavior of the empirical multilinear copula process under broad conditions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 82-110.
  46. Bien, Katarzyna & Nolte, Ingmar & Pohlmeier, Winfried, 2006. "Estimating liquidity using information on the multivariate trading process," CoFE Discussion Papers 06/04, University of Konstanz, Center of Finance and Econometrics (CoFE).
  47. Michel Denuit & Anna Kiriliouk & Johan Segers, 2014. "Max-factor individual risk models with application to credit portfolios," Papers 1412.3230, arXiv.org.
  48. Denuit, Michel & Mesfioui, Mhamet & Trufin, Julien, 2016. "Bounds on Concordance-Based Validation Statistics in Regression Models for Binary Responses," LIDAM Discussion Papers ISBA 2016046, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  49. Emanuela Raffinetti & Fabio Aimar, 2019. "MDCgo takes up the association/correlation challenge for grouped ordinal data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(4), pages 527-561, December.
  50. Tzougas, George & di Cerchiara, Alice Pignatelli, 2021. "Bivariate mixed Poisson regression models with varying dispersion," LSE Research Online Documents on Economics 114327, London School of Economics and Political Science, LSE Library.
  51. Mhamed Mesfioui & Julien Trufin, 2022. "Bounds on Multivariate Kendall’s Tau and Spearman’s Rho for Zero-Inflated Continuous Variables and their Application to Insurance," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 1051-1059, June.
  52. Jong-Min Kim & Hyunsu Ju & Yoonsung Jung, 2020. "Copula Approach for Developing a Biomarker Panel for Prediction of Dengue Hemorrhagic Fever," Annals of Data Science, Springer, vol. 7(4), pages 697-712, December.
  53. Tzougas, George & Makariou, Despoina, 2022. "The multivariate Poisson-Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters," LSE Research Online Documents on Economics 117197, London School of Economics and Political Science, LSE Library.
  54. Geenens Gery, 2020. "Copula modeling for discrete random vectors," Dependence Modeling, De Gruyter, vol. 8(1), pages 417-440, January.
  55. Youngmi Lee & Sangyeol Lee & Dag Tjøstheim, 2018. "Asymptotic normality and parameter change test for bivariate Poisson INGARCH models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 52-69, March.
  56. Jonas Moss & Steffen Grønneberg, 2023. "Partial Identification of Latent Correlations with Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 241-252, March.
  57. Quinn C, 2009. "Measuring income-related inequalities in health using a parametric dependence function," Health, Econometrics and Data Group (HEDG) Working Papers 09/24, HEDG, c/o Department of Economics, University of York.
  58. Pimentel, Ronald S. & Niewiadomska-Bugaj, Magdalena & Wang, Jung-Chao, 2015. "Association of zero-inflated continuous variables," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 61-67.
  59. Chavez-Demoulin, V. & Embrechts, P. & Neslehova, J., 2006. "Quantitative models for operational risk: Extremes, dependence and aggregation," Journal of Banking & Finance, Elsevier, vol. 30(10), pages 2635-2658, October.
  60. Denuit, Michel & Kiriliouk, Anna & Segers, Johan, 2014. "Max-Factor individual risk models with application to credit portfolios," LIDAM Discussion Papers ISBA 2014048, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  61. Zilko, Aurelius A. & Kurowicka, Dorota, 2016. "Copula in a multivariate mixed discrete–continuous model," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 28-55.
  62. Faugeras, Olivier P., 2013. "Sklar’s theorem derived using probabilistic continuation and two consistency results," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 271-277.
  63. Michel Denuit & Mhamed Mesfioui & Julien Trufin, 2019. "Bounds on Concordance-Based Validation Statistics in Regression Models for Binary Responses," Methodology and Computing in Applied Probability, Springer, vol. 21(2), pages 491-509, June.
  64. L. Madsen & Y. Fang, 2011. "Joint Regression Analysis for Discrete Longitudinal Data," Biometrics, The International Biometric Society, vol. 67(3), pages 1171-1175, September.
  65. Wei, Zheng & Wang, Li & Liao, Shu-Min & Kim, Daeyoung, 2023. "On the exploration of regression dependence structures in multidimensional contingency tables with ordinal response variables," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
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