An Online Expectation-Maximization Algorithm for Changepoint Models

Changepoint models are widely used to model the heterogeneity of sequential data. We present a novel sequential Monte Carlo (SMC) online expectation-maximization (EM) algorithm for estimating the static parameters of such models. The SMC online EM algorithm has a cost per time which is linear in the...

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Bibliographic Details
Published inJournal of computational and graphical statistics Vol. 22; no. 4; pp. 906 - 926
Main Authors Yildirim, Sinan, Singh, Sumeetpal S., Doucet, Arnaud
Format Journal Article
LanguageEnglish
Published Alexandria Taylor & Francis Group 01.12.2013
American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Taylor & Francis Ltd
Subjects
Algorithms
Asymptotic methods
Computational Statistics Pot Pourri
Mathematical models
Monte Carlo simulation
Parameter estimation
Particle filter
Recursive estimation
Smoothing
Stochastic approximation
Studies
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Summary:Changepoint models are widely used to model the heterogeneity of sequential data. We present a novel sequential Monte Carlo (SMC) online expectation-maximization (EM) algorithm for estimating the static parameters of such models. The SMC online EM algorithm has a cost per time which is linear in the number of particles and could be particularly important when the data is representable as a long sequence of observations, since it drastically reduces the computational requirements for implementation. We present an asymptotic analysis for the stability of the SMC estimates used in the online EM algorithm and demonstrate the performance of this scheme by using both simulated and real data originating from DNA analysis. The supplementary materials for the article are available online.
ISSN:1061-8600
1537-2715
DOI:10.1080/10618600.2012.674653