Distributed inference for the extreme value index
成果类型：
Article
署名作者：
Chen, Liujun; Li, Deyuan; Zhou, Chen
署名单位：
Fudan University; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asab001
发表日期：
2022
页码：
257264
关键词：
摘要：
In this paper we investigate a divide-and-conquer algorithm for estimating the extreme value index when data are stored in multiple machines. The oracle property of such an algorithm based on extreme value methods is not guaranteed by the general theory of distributed inference. We propose a distributed Hill estimator and establish its asymptotic theories. We consider various cases where the number of observations involved in each machine can be either homogeneous or heterogeneous, and either fixed or varying according to the total sample size. In each case we provide a sufficient, sometimes also necessary, condition under which the oracle property holds.