A hidden Markov filtering approach to multiple change-point models

Author

Lai, Tze Leung ; Xing, Haipeng

Author_Institution

Dept. of Stat., Stanford Univ., Palo Alto, CA, USA

fYear

2008

fDate

9-11 Dec. 2008

Firstpage

1914

Lastpage

1919

Abstract

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.

Keywords

hidden Markov models; piecewise constant techniques; recursive filters; smoothing methods; statistical analysis; adaptive control; array-CGH data analysis; cancer; computational property; estimation; genetic study; hidden Markov filtering approach; multiple change-point models; online detection; piecewise constant parameters; recursive filters; smoothers; statistical property; stochastic systems; Adaptive arrays; Adaptive control; Cancer detection; Data analysis; Filtering; Filters; Genetics; Hidden Markov models; Recursive estimation; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Cancun

ISSN

0191-2216

Print_ISBN

978-1-4244-3123-6

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2008.4739184

Filename

4739184