A Monte Carlo Markov chain algorithm for a class of mixture time series models |
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Authors: | John W Lau Mike K P So |
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Institution: | 1.School of Mathematics and Statistics,University of Western Australia,Perth,Australia;2.Department of Information Systems, Business Statistics and Operations Management,Hong Kong University of Science and Technology,Hong Kong,Hong Kong |
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Abstract: | This article generalizes the Monte Carlo Markov Chain (MCMC) algorithm, based on the Gibbs weighted Chinese restaurant (gWCR)
process algorithm, for a class of kernel mixture of time series models over the Dirichlet process. This class of models is
an extension of Lo’s (Ann. Stat. 12:351–357, 1984) kernel mixture model for independent observations. The kernel represents a known distribution of time series conditional
on past time series and both present and past latent variables. The latent variables are independent samples from a Dirichlet
process, which is a random discrete (almost surely) distribution. This class of models includes an infinite mixture of autoregressive
processes and an infinite mixture of generalized autoregressive conditional heteroskedasticity (GARCH) processes. |
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Keywords: | |
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