An Online Expectation-Maximization Algorithm for Changepoint Models

Journal article

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 fromDNAanalysis. The supplementary materials for the article are available online. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Authors: Yildirim, S; Singh, S; Doucet, A

• Publication status: Published
• Journal: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
• Volume: 22
• Issue: 4
• Pages: 906-926
• Publication date: 2013-12-01
• DOI: 10.1080/10618600.2012.674653
• EISSN: 1537-2715
• ISSN: 1061-8600
• Keywords: Smoothing; Stochastic approximation; Particle filter; Recursive estimation