Full likelihood inference for max-stable data

被引：30

作者：

Huser, Raphael ^{[1
]}

Dombry, Clement ^{[2
]}

Ribatet, Mathieu ^{[3
]}

Genton, Marc G. ^{[1
]}

机构：

[1] KAUST, CEMSE Div, Thuwal 239556900, Saudi Arabia

[2] Univ Franche Comte, Dept Math, Besancon, France

[3] Univ Montpellier, Dept Math, Montpellier 5, France

来源：

STAT | 2019年 / 8卷 / 01期

关键词：

full likelihood; max-stable distribution; Stephenson-Tawn likelihood; stochastic expectation-maximization algorithm; COMPOSITE LIKELIHOOD; BAYESIAN-INFERENCE; OCCURRENCE TIMES; SIMULATION; ESTIMATORS; MODEL;

D O I：

10.1002/sta4.218

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We show how to perform full likelihood inference for max-stable multivariate distributions or processes based on a stochastic expectation-maximization algorithm, which combines statistical and computational efficiency in high dimensions. The good performance of this methodology is demonstrated by simulation based on the popular logistic and Brown-Resnick models, and it is shown to provide computational time improvements with respect to a direct computation of the likelihood. Strategies to further reduce the computational burden are also discussed.

引用

页数：14

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