Distributed inference for the extreme value index

Liujun Chen, Deyuan Li, Chen Zhou

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)257-264
Number of pages8
JournalBiometrika
Volume109
Issue number1
DOIs
Publication statusPublished - 1 Mar 2022

Bibliographical note

Publisher Copyright:
© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the Biometrika Trust. All rights reserved.

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