Estimating the locations of multiple change points in the mean
type: Item
instance of: scholarly article
title: Estimating the locations of multiple change points in the mean
zbMATH Open document ID: 1010.62018
author: Joe H. Sullivan
published in: Computational Statistics
publication date: 2003-02-06T00:00:00Z
review text: This article deals with the problem of detecting one or more change points. Suppose that there are \(n\) independent observations \(x_{1},\ldots, x_{n}\) from normal distributions all having the same variance. There are \(R\) shifts in the mean subject to \(0\leq R<R_{u}<n\). The location of each shift is \(\tau_{r},\;r=1,\ldots,R\), with \(0<\tau_1<\ldots<\tau_{R}<n\). Given the data and \(R_{u}\) we want to estimate \(R\) and the location of the changes \(\tau_{r}\). A new algorithm is introduced for identifying locations of change points. These location estimates make it computationally feasible to apply the Schwarz information criterion identifying the number of change points maximizing the criterion. For comparisons the single shift method is used with binary segmentation and the proposed method is shown to be superior in detecting two shifts.
Mathematics Subject Classification ID: 62F10
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 1865453
zbMATH Keywords: locations
zbMATH Keywords: multiple change points
zbMATH Keywords: mean
zbMATH Keywords: normal distributions
zbMATH Keywords: Schwarz information criterion
reviewed by: Aleksandr D. Borisenko
MaRDI profile type: Publication
full work available at URL: s001800200107
OpenAlex ID: W2913836000
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DOI: 10.1007/S001800200107
description: scientific article; zbMATH DE number 1865453