A Nonparametric Approach for Multiple Change-Point Analysis of Multivariate Data

• Activity Number: 160
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): David Scott Matteson*+ and Nicholas A James
• Companies: Cornell University and Cornell University
• Keywords: Cluster analysis ; Nonparametric statistics ; Permutation tests ; Time Series ; U-statistics

Abstract

Change point analysis has applications in a wide variety of fields. The general problem concerns the inference of a change in the distribution for a set of time-ordered observations. Sequential detection is an online version in which new data is continually arriving and is 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, 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 first absolute moments. Estimation is based on hierarchical clustering and we propose both divisive and agglomerative algorithms. We conclude with applications in finance, operations, and genetics.