Dirichlet process hidden Markov multiple change-point model
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AbstractThis paper proposes a new Bayesian multiple change-point model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of change-points a priori. Hence our model is robust to model specification in contrast to the fully parametric Bayesian model. We propose a general Markov chain Monte Carlo algorithm which only needs to sample the states around change-points. Simulations for a normal mean-shift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coal-mining disaster data and the real United States Gross Domestic Product growth, are provided. We detect a single change-point for both the disaster data and US GDP growth. All the change-point locations and posterior inferences of the two applications are in line with existing methods.
All Author(s) ListKo S.I.M., Chong T.T.L., Ghosh P.
Journal nameBayesian Analysis
Year2015
Month1
Day1
Volume Number10
Issue Number2
PublisherCarnegie Mellon University
Place of PublicationUnited States
Pages275 - 296
ISSN1936-0975
LanguagesEnglish-United Kingdom
KeywordsChange-point, Dirichlet process, Hidden markov model, Markov chain monte carlo, Nonparametric bayesian

Last updated on 2020-24-05 at 00:13