A New Point Process Regression Extreme Model Using a Dirichlet Process Mixture of Weibull Distribution
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Published:2022-10-13
Issue:20
Volume:10
Page:3781
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Wang YingjieORCID,
Liu XinshengORCID
Abstract
The extreme value theory is widely used in economic and environmental domains, it aims to study the stochastic extreme behaviors associated with rare events. In this context, we consider a new mixture model for extremal events analysis, including a Dirichlet process mixture of Weibull (DPMW) distribution below the threshold and the point process (PP) extreme model for the upper tail. This model developed a regression structure for the PP extreme model parameters, which explains the variation of the exceedance through all tail parameters. The estimation of the model parameters is performed under the Bayesian paradigm, applying the Markov chains Monte Carlo (MCMC) method. The model is applied to both simulation and real environmental data to demonstrate the performance in extrapolating extreme events.
Funder
National Natural Science Foundation of China
Postgraduate Research & Practice Innovation Program of Jiangsu Province
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
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