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Univariate mean change point detection : penalization, CUSUM and optimality

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Wang, Daren, Yu, Yi and Rinaldo, Alessandro (2020) Univariate mean change point detection : penalization, CUSUM and optimality. Electronic Journal of Statistics, 14 (1). pp. 1917-1961. doi:10.1214/20-EJS1710 ISSN 1935-7524.

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Official URL: https://doi.org/10.1214/20-EJS1710

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Abstract

The problem of univariate mean change point detection and localization based on a sequence of n independent observations with piecewise constant means has been intensively studied for more than half century, and serves as a blueprint for change point problems in more complex settings. We provide a complete characterization of this classical problem in a general framework in which the upper bound σ2 on the noise variance, the minimal spacing Δ between two consecutive change points and the minimal magnitude κ of the changes, are allowed to vary with n. We first show that consistent localization of the change points, when the signal-to-noise ratio κΔ√σ<log(n)−−−−−√, is impossible. In contrast, when κΔ√σ diverges with n at the rate of at least log(n)−−−−−√, we demonstrate that two computationally-efficient change point estimators, one based on the solution to an ℓ0-penalized least squares problem and the other on the popular wild binary segmentation algorithm, are both consistent and achieve a localization rate of the order σ2κ2log(n). We further show that such rate is minimax optimal, up to a log(n) term.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Analysis of variance, Mathematical statistics, Change-point problems , CUSUM technique, Binary system (Mathematics)
Journal or Publication Title: Electronic Journal of Statistics
Publisher: Institute of Mathematical Statistics
ISSN: 1935-7524
Official Date: 28 April 2020
Dates:
DateEvent
28 April 2020Published
1 April 2020Accepted
Volume: 14
Number: 1
Page Range: pp. 1917-1961
DOI: 10.1214/20-EJS1710
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 2 April 2020
Date of first compliant Open Access: 28 April 2020
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