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A hidden Markov filtering approach to multiple change-point models

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2 Author(s)
Tze Leung Lai ; Dept. of Stat., Stanford Univ., Palo Alto, CA, USA ; Haipeng Xing

We describe a hidden Markov modeling approach to multiple change-points that has attractive computational and statistical properties. This approach yields explicit recursive filters and smoothers for estimating the piecewise constant parameters. Applications to array-CGH data analysis in genetic studies of cancer and to on-line detection, estimation and adaptive control of stochastic systems whose parameters may undergo occasional changes are given to illustrate the versatility of the proposed methodology.

Published in:

Decision and Control, 2008. CDC 2008. 47th IEEE Conference on

Date of Conference:

9-11 Dec. 2008