Title

Detection Of Multiple Change-Points In Multivariate Data

Keywords

binary splitting; dynamic programming; principle of optimality; regression trees; separability

Abstract

The statistical analysis of change-point detection and estimation has received much attention recently. A time point such that observations follow a certain statistical distribution up to that point and a different distribution - commonly of the same functional form but different parameters after that point - is called a change-point. Multiple change-point problems arise when we have more than one change-point. This paper develops a method for multivariate normally distributed data to detect change-points and estimate within-segment parameters using maximum likelihood estimation. © 2013 Copyright Taylor and Francis Group, LLC.

Publication Date

9-1-2013

Publication Title

Journal of Applied Statistics

Volume

40

Issue

9

Number of Pages

1979-1995

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/02664763.2013.800471

Socpus ID

84883656217 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84883656217

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