MODELING PANELS OF EXTREMES

• 成果类型: Article
• 署名作者: Dupuis, Debbie J.; Engelke, Sebastian; Trapin, Luca
• 署名单位: Universite de Montreal; HEC Montreal; University of Geneva; University of Bologna
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1639SUPP
• 发表日期: 2023
• 页码: 498-517
• 关键词: MAXIMUM-LIKELIHOOD ESTIMATORS; regionalization; temperature; inference; depth; peaks

摘要

Extreme value applications commonly employ regression techniques to capture cross-sectional heterogeneity or time variation in the data. Estimation of the parameters of an extreme value regression model is notoriously challenging due to the small number of observations that are usually available in applications. When repeated extreme measurements are collected on the same individuals, that is, a panel of extremes is available, pooling the observations in groups can improve the statistical inference. We study three data sets related to risk assessment in finance, climate science, and hydrology. In all three cases the problem can be formulated as an extreme value panel re-gression model with a latent group structure and group-specific parameters. We propose a new algorithm that jointly assigns the individuals to the la-tent groups and estimates the parameters of the regression model inside each group. Our method efficiently recovers the underlying group structure with-out prior information, and for the three data sets it provides improved return level estimates and helps answer important domain-specific questions.