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DAVID MATTESON - Cornell University

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” A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data”
13 September 2012 from 4:00 PM to 5:00 PM
111 Tyson Bldg.
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Dave MattesonChange 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 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 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 pth absolute moment, for some p in (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 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. Applications in
finance, genetics and spatio-temporal analysis are presented. We
conclude with a discussion of future work.

This talk is based on joint work with Nicholas A. James.

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