Dirichlet process hidden markov multiple change-point model
Document Type
Article
Publication Title
Bayesian Analysis
Abstract
This 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.
DOI Link
Publication Date
1-6-2015
Publisher
Project Euclid
Volume
Vol.10
Issue
Iss.2
Recommended Citation
Ko, Stanley I M; Chong, Terence T L; and Ghosh, Pulak, "Dirichlet process hidden markov multiple change-point model" (2015). Faculty Publications. 677.
https://research.iimb.ac.in/fac_pubs/677
