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
收藏
页数:14
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