A hidden markov filtering approach to multiple change-point models

Tze Leung Lai; Haipeng Xing

doi:10.1109/CDC.2008.4739184

A hidden markov filtering approach to multiple change-point models

Tze Leung Lai
, Haipeng Xing

Applied Mathematics and Statistics

Stanford University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1914-1919
Number of pages	6
ISBN (Print)	9781424431243
DOIs	https://doi.org/10.1109/CDC.2008.4739184
State	Published - 2008
Event	47th IEEE Conference on Decision and Control, CDC 2008 - Cancun, Mexico Duration: Dec 9 2008 → Dec 11 2008

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	47th IEEE Conference on Decision and Control, CDC 2008
Country/Territory	Mexico
City	Cancun
Period	12/9/08 → 12/11/08

Access to Document

10.1109/CDC.2008.4739184

Cite this

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title = "A hidden markov filtering approach to multiple change-point models",

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.",

author = "Lai, \{Tze Leung\} and Haipeng Xing",

year = "2008",

doi = "10.1109/CDC.2008.4739184",

language = "English",

isbn = "9781424431243",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1914--1919",

booktitle = "Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008",

note = "47th IEEE Conference on Decision and Control, CDC 2008 ; Conference date: 09-12-2008 Through 11-12-2008",

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Lai, TL & Xing, H 2008, A hidden markov filtering approach to multiple change-point models. in Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008., 4739184, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 1914-1919, 47th IEEE Conference on Decision and Control, CDC 2008, Cancun, Mexico, 12/9/08. https://doi.org/10.1109/CDC.2008.4739184

A hidden markov filtering approach to multiple change-point models. / Lai, Tze Leung; Xing, Haipeng.
Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008. Institute of Electrical and Electronics Engineers Inc., 2008. p. 1914-1919 4739184 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Xing, Haipeng

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N2 - 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.

AB - 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.

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U2 - 10.1109/CDC.2008.4739184

DO - 10.1109/CDC.2008.4739184

M3 - Conference contribution

AN - SCOPUS:62949190644

SN - 9781424431243

T3 - Proceedings of the IEEE Conference on Decision and Control

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EP - 1919

BT - Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 47th IEEE Conference on Decision and Control, CDC 2008

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ER -

A hidden markov filtering approach to multiple change-point models

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