A Hidden Markov Filtering Approach to Multiple Change-point Models

被引：0

作者：

Lai, Tze Leung ^{[1
]}

Xing, Haipeng ^{[2
]}

机构：

[1] Stanford Univ, Dept Stat, Stanford, CA 94305 USA

[2] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA

来源：

47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008) | 2008年

基金：

美国国家科学基金会;

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：1914 / 1919

页数：6

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