A hidden Markov filtering approach to multiple change-point models

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.

• Type: Conference
• Year: 2008
• Where: CDC
• Authors: Tze Leung Lai, Haipeng Xing
• Keywords: Array-CGH Data Analysis, CDC 2008, Control Systems, Markov Modeling Approach, Piecewise Constant Parameters