Dirichlet Process Hidden Markov Multiple Change-point Model

Ko, Stanley I. M. and Chong, Terence T. L. and Ghosh, Pulak (2014): Dirichlet Process Hidden Markov Multiple Change-point Model. Forthcoming in: 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 US GDP 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Change-point; Dirichlet process; Hidden Markov model; Markov chain; Monte Carlo; Nonparametric Bayesian.
• Subjects: C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes

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