BIC Applied to Model Selection of a Large Number of Change-points

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  • Date/Time:
    • Wednesday April 7, 2010
      12:00 pm - 1:00 pm
  • Location: ISyE Executive classroom
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Summary Sentence: BIC Applied to Model Selection of a Large Number of Change-points

Full Summary: BIC Applied to Model Selection of a Large Number of Change-points

TITLE: BIC Applied to Model Selection of a Large Number of Change-points

SPEAKER: Professor David Siegmund

ABSTRACT:

In a previous paper (Biometrics, 2006, pp. 22-32) we derived a Bayes Information Criterion (BIC) for determining the number of change-points in a sequence of independent observations when the number $m$ of change-points is assumed to remain bounded as the number of observations increases. Here we generalize that result to include multiple aligned sequences with intervals of simultaneous change that occur in a fraction of the sequences and a total number of of change-points $m$ that can increase with the sample size; and we include in the criterion terms that increase at rate $m$. Stochastic terms that enter into the new criterion involve integrals and maxima of two-sided Brownian motion with negative drift. Examples involve segmenting aligned DNA sequences according to copy number variations that occur at the same position in a fraction of the sequences.
This is joint research with N. Zhang.

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  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Mar 18, 2010 - 6:56am
  • Last Updated: Oct 7, 2016 - 9:51pm