Statistical inference for max-stable processes in space and time
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Cited by:
- Wang, Yixin & So, Mike K.P., 2016. "A Bayesian hierarchical model for spatial extremes with multiple durations," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 39-56.
- Abhimanyu Gupta & Javier Hidalgo, 2020.
"Nonparametric prediction with spatial data,"
Papers
2008.04269, arXiv.org, revised Nov 2021.
- Gupta, Abhimanyu & Hidalgo, Javier, 2022. "Nonparametric prediction with spatial data," LSE Research Online Documents on Economics 115292, London School of Economics and Political Science, LSE Library.
- Abhimanyu Gupta & Javier Hidalgo, 2022. "Nonparametric prediction with spatial data," STICERD - Econometrics Paper Series 621, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Einmahl, J.H.J. & Kiriliouk, A. & Krajina, A. & Segers, J., 2014. "An M-estimator of Spatial Tail Dependence," Other publications TiSEM 2d5c1a3b-a5f6-4329-8df2-f, Tilburg University, School of Economics and Management.
- Raphaël Huser & Marc G. Genton, 2016. "Non-Stationary Dependence Structures for Spatial Extremes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(3), pages 470-491, September.
- 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.
- Einmahl, J.H.J. & Kiriliouk, A. & Krajina, A. & Segers, J., 2014. "An M-estimator of Spatial Tail Dependence," Discussion Paper 2014-021, Tilburg University, Center for Economic Research.
- Einmahl, John & Kiriliouk, Anna & Krajina, Andrea & Segers, Johan, 2016. "An M-estimator of spatial tail dependence," LIDAM Reprints ISBA 2016004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
- 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).
- A. Abu-Awwad & V. Maume-Deschamps & P. Ribereau, 2021. "Semiparametric estimation for space-time max-stable processes: an F-madogram-based approach," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 241-276, July.
- Damek, Ewa & Mikosch, Thomas & Zhao, Yuwei & Zienkiewicz, Jacek, 2023. "Whittle estimation based on the extremal spectral density of a heavy-tailed random field," Stochastic Processes and their Applications, Elsevier, vol. 155(C), pages 232-267.
- Hugo C. Winter & Jonathan A. Tawn, 2016. "Modelling heatwaves in central France: a case-study in extremal dependence," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(3), pages 345-365, April.
- Das, Bikramjit & Engelke, Sebastian & Hashorva, Enkelejd, 2015. "Extremal behavior of squared Bessel processes attracted by the Brown–Resnick process," Stochastic Processes and their Applications, Elsevier, vol. 125(2), pages 780-796.
- Yong Bum Cho & Richard A. Davis & Souvik Ghosh, 2016. "Asymptotic Properties of the Empirical Spatial Extremogram," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 757-773, September.
- Buhl, Sven & Klüppelberg, Claudia, 2018. "Limit theory for the empirical extremogram of random fields," Stochastic Processes and their Applications, Elsevier, vol. 128(6), pages 2060-2082.
- Michele Nguyen & Almut E. D. Veraart, 2017. "Spatio-temporal Ornstein–Uhlenbeck Processes: Theory, Simulation and Statistical Inference," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 46-80, March.
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