OPTIMAL CHANGE-POINT ESTIMATION IN TIME SERIES

被引：1

作者：

Chan, Ngai Hang ^{[1
]}

Ng, Wai Leong ^{[2
]}

Yau, Chun Yip ^{[1
]}

Yu, Haihan ^{[3
]}

机构：

[1] Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China

[2] Hang Seng Univ Hong Kong, Dept Math Stat & Insurance, Hong Kong, Peoples R China

[3] Iowa State Univ, Dept Stat, Ames, IA USA

来源：

ANNALS OF STATISTICS | 2021年 / 49卷 / 04期

关键词：

Bayes-type estimator; confidence interval; double-sided random process; piecewise stationary time series; structural break; BOOTSTRAP; GARCH; CALIBRATION;

D O I：

10.1214/20-AOS2039

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：

This paper establishes asymptotic theory for optimal estimation of change points in general time series models under alpha-mixing conditions. We show that the Bayes-type estimator is asymptotically minimax for change-point estimation under squared error loss. Two bootstrap procedures are developed to construct confidence intervals for the change points. An approximate limiting distribution of the change-point estimator under small change is also derived. Simulations and real data applications are presented to investigate the finite sample performance of the Bayes-type estimator and the bootstrap procedures.

引用

页码：2336 / 2355

页数：20

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