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Dirichlet process hidden Markov multiple change-point model. (English) Zbl 1335.62052

Summary: 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.

MSC:

62F15 Bayesian inference
62G10 Nonparametric hypothesis testing
62M02 Markov processes: hypothesis testing
62P20 Applications of statistics to economics
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References:

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