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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, July 17, 2007
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Haipeng Xing} \\
  Department of Statistics\\
  Columbia University
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  \subsection*{A hidden Markov modeling approach to multiple
change-points}
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\noindent

After a brief review of previous frequentist and Bayesian approaches to
multiple change-points, we describe a hidden Markov modeling approach that
has attractive computational and statistical properties. This approach
yields explicit recursive filters and smoothers for estimating the
piecewise constant parameters in multiparameter exponential families and
generalized linear models, and efficient estimators of the hyperparameters
of the hidden Markov model for the parameter jumps. Although the
approach is Bayesian in nature, it can be used for frequentist problems
such as significance testing of the null hypothesis of no change-points
versus multiple change-point alternatives. It can also be used to partition
the unknown parameter sequence into segments of equal values and to
provide confidence assessment of the segmentation. Applications to
array-CGH data analysis in genetic studies of cancer, change-point
AR-GARCH modeling of econometric time series, and speech recognitions
illustrate the versatility of the proposed methodology. Simulation studies and
asymptotic theory of its performance are also given.


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