Ko, Stanley I. M. and Chong, Terence T. L. and Ghosh, Pulak (2014): Dirichlet Process Hidden Markov Multiple Changepoint Model. Forthcoming in: Bayesian Analysis

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Abstract
This paper proposes a new Bayesian multiple changepoint model which is based on the hidden Markov approach. The Dirichlet process hidden Markov model does not require the specification of the number of changepoints 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 changepoints. Simulations for a normal meanshift model with known and unknown variance demonstrate advantages of our approach. Two applications, namely the coalmining disaster data and the real US GDP growth, are provided. We detect a single changepoint for both the disaster data and US GDP growth. All the changepoint locations and posterior inferences of the two applications are in line with existing methods.
Item Type:  MPRA Paper 

Original Title:  Dirichlet Process Hidden Markov Multiple Changepoint Model 
Language:  English 
Keywords:  Changepoint; Dirichlet process; Hidden Markov model; Markov chain; Monte Carlo; Nonparametric Bayesian. 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes 
Item ID:  57871 
Depositing User:  Terence T L Chong 
Date Deposited:  17 Aug 2014 01:26 
Last Modified:  03 Oct 2019 14:41 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/57871 