Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks
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[1] B. Shaby,et al. Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process , 2022, 2208.03344.
[2] J. Jalbert,et al. A flexible extended generalized Pareto distribution for tail estimation , 2022, Environmetrics.
[3] C. Wikle,et al. Statistical Deep Learning for Spatial and Spatio-Temporal Data , 2022, Annual Review of Statistics and Its Application.
[4] Raphael Huser,et al. A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data , 2021, 2112.09530.
[5] G. Meehl,et al. Decadal climate variability in the tropical Pacific: Characteristics, causes, predictability, and prospects , 2021, Science.
[6] Johann Rudi,et al. Neural networks for parameter estimation in intractable models , 2021, Comput. Stat. Data Anal..
[7] H. Rue,et al. Practical strategies for GEV-based regression models for extremes , 2021, 2106.13110.
[8] Douglas W. Nychka,et al. Fast covariance parameter estimation of spatial Gaussian process models using neural networks , 2020, Stat.
[9] Yanan Fan,et al. Overview of ABC , 2018, Handbook of Approximate Bayesian Computation.
[10] H. Rue,et al. INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles , 2018, Extremes.
[11] Dirk Roos,et al. Deep Gaussian Covariance Network , 2017, ArXiv.
[12] M. Creel. Neural nets for indirect inference , 2017 .
[13] Johan Segers,et al. On the maximum likelihood estimator for the Generalized Extreme-Value distribution , 2016, 1601.05702.
[14] Elisabeth J. Moyer,et al. Estimating changes in temperature extremes from millennial scale climate simulations using generalized extreme value (GEV) distributions , 2015, 1512.08775.
[15] Bai Jiang,et al. Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network , 2015, 1510.02175.
[16] Anthony C. Davison,et al. Statistics of Extremes , 2015, International Encyclopedia of Statistical Science.
[17] Scott A. Sisson,et al. Modelling extremes using approximate Bayesian Computation , 2014, 1411.1451.
[18] Michael Creel,et al. Indirect likelihood inference (revised) , 2013 .
[19] Richard L. Smith,et al. Approximate Bayesian computing for spatial extremes , 2011, Comput. Stat. Data Anal..
[20] Richard W. Katz,et al. Statistics of extremes in climate change , 2010 .
[21] John A. Nelder,et al. Nelder-Mead algorithm , 2009, Scholarpedia.
[22] T. Ouarda,et al. Joint Bayesian model selection and parameter estimation of the generalized extreme value model with covariates using birth‐death Markov chain Monte Carlo , 2009 .
[23] M. Blum. Approximate Bayesian Computation: A Nonparametric Perspective , 2009, 0904.0635.
[24] D. Nychka,et al. Bayesian Spatial Modeling of Extreme Precipitation Return Levels , 2007 .
[25] W. Collins,et al. The Community Climate System Model Version 3 (CCSM3) , 2006 .
[26] W. Adger,et al. Social Capital, Collective Action, and Adaptation to Climate Change , 2003 .
[27] Eric P. Smith,et al. An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.
[28] J. Stedinger,et al. Generalized maximum‐likelihood generalized extreme‐value quantile estimators for hydrologic data , 2000 .
[29] Stuart G. Coles,et al. Spatial Regression Models for Extremes , 1999 .
[30] G. Geoffrey Booth,et al. The behavior of extreme values in Germany's stock index futures: An application to intradaily margin setting , 1998 .
[31] R.J. Cohen,et al. Linear and nonlinear ARMA model parameter estimation using an artificial neural network , 1997, IEEE Transactions on Biomedical Engineering.
[32] J. Angus. Extreme Value Theory in Engineering , 1990 .
[33] J. R. Wallis,et al. Estimation of the generalized extreme-value distribution by the method of probability-weighted moments , 1985 .
[34] Richard L. Smith. Maximum likelihood estimation in a class of nonregular cases , 1985 .
[35] A. Jenkinson. The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .
[36] R. Fisher,et al. Limiting forms of the frequency distribution of the largest or smallest member of a sample , 1928, Mathematical Proceedings of the Cambridge Philosophical Society.
[37] A. Zammit‐Mangion,et al. Fast Optimal Estimation with Intractable Models using Permutation-Invariant Neural Networks , 2022 .
[38] R. Huser,et al. A unifying partially-interpretable framework for neural network-based extreme quantile regression , 2022, ArXiv.
[39] J. Kyselý. NOTES AND CORRESPONDENCE A Cautionary Note on the Use of Nonparametric Bootstrap for Estimating Uncertainties in Extreme-Value Models , 2008 .
[40] Richard L. Smith. EXTREME VALUE THEORY , 2008 .
[41] M. Kenward,et al. An Introduction to the Bootstrap , 2007 .
[42] Daniel T. Kaplan,et al. Introduction to Statistical Modeling , 2005 .
[43] Vijay P. Singh,et al. Generalized Extreme Value Distribution , 1998 .
[44] Richard L. Smith. Weibull regression models for reliability data , 1991 .