EconPapers    
Economics at your fingertips  
 

A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data

David S. Matteson and Nicholas A. James

Journal of the American Statistical Association, 2014, vol. 109, issue 505, 334-345

Abstract: Change point analysis has applications in a wide variety of fields. The general problem concerns the inference of a change in distribution for a set of time-ordered observations. Sequential detection is an online version in which new data are continually arriving and are analyzed adaptively. We are concerned with the related, but distinct, offline version, in which retrospective analysis of an entire sequence is performed. For a set of multivariate observations of arbitrary dimension, we consider nonparametric estimation of both the number of change points and the positions at which they occur. We do not make any assumptions regarding the nature of the change in distribution or any distribution assumptions beyond the existence of the αth absolute moment, for some α is an element of (0, 2). Estimation is based on hierarchical clustering and we propose both divisive and agglomerative algorithms. The divisive method is shown to provide consistent estimates of both the number and the location of change points under standard regularity assumptions. We compare the proposed approach with competing methods in a simulation study. Methods from cluster analysis are applied to assess performance and to allow simple comparisons of location estimates, even when the estimated number differs. We conclude with applications in genetics, finance, and spatio-temporal analysis. Supplementary materials for this article are available online.

Date: 2014
References: View complete reference list from CitEc
Citations View citations in EconPapers (9) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2013.849605 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:109:y:2014:i:505:p:334-345

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2018-04-14
Handle: RePEc:taf:jnlasa:v:109:y:2014:i:505:p:334-345