Bayesian Analysis

Dirichlet Process Hidden Markov Multiple Change-point Model

Stanley I. M. Ko, Terence T. L. Chong, and Pulak Ghosh

Full-text: Open access

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.

Article information

Source
Bayesian Anal., Volume 10, Number 2 (2015), 275-296.

Dates
First available in Project Euclid: 2 February 2015

Permanent link to this document
https://projecteuclid.org/euclid.ba/1422884975

Digital Object Identifier
doi:10.1214/14-BA910

Mathematical Reviews number (MathSciNet)
MR3420883

Zentralblatt MATH identifier
1335.62052

Keywords
Change-point Dirichlet process Hidden Markov model Markov chain Monte Carlo Nonparametric Bayesian

Citation

Ko, Stanley I. M.; Chong, Terence T. L.; Ghosh, Pulak. Dirichlet Process Hidden Markov Multiple Change-point Model. Bayesian Anal. 10 (2015), no. 2, 275--296. doi:10.1214/14-BA910. https://projecteuclid.org/euclid.ba/1422884975


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