This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 636
Type: Contributed
Date/Time: Thursday, August 5, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #308692
Title: Exact Distributions and Sequential Monte Carlo for Change Points in Space-Time Systems with Application to Brain Imaging
Author(s): John Aston*+ and Christopher Nam and Adam Johansen
Companies: University of Warwick and University of Warwick and University of Warwick
Address: , Coventry, International, CV4 7AL, UK
Keywords: Hidden Markov Models ; Markov chain imbedding ; Recursive Computation ; Computational Statistics

Quantifying the uncertainty in the locations of change points is a topic of significant interest. However, when additional spatial information is also available such that change points are likely to occur at similar times in similar locations, detection can be enhanced and uncertainty reduced. A new methodology is proposed to quantify change points in massive data sets such as functional brain imaging studies using hidden Markov models. This method is based on using Markov chain imbedding to generate exact distributions of change point locations for particular parameter estimates of the HMM and then using sequential monte carlo to sample the parameter distribution. Using this approach dramatically reduces the search space, as state estimates for each time point are no longer required, particularly if only a small number of change points likely occur.

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