A Monte Carlo Markov chain algorithm for a class of mixture time series models

John W.Lau; Mike K.P. So

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A Monte Carlo Markov chain algorithm for a class of mixture time series models

机译：一类混合时间序列模型的Monte Carlo Markov链算法

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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.

机译：本文基于Gibbs加权中餐馆（gWCR）过程算法，对Dirichlet过程上一类时间序列模型的核混合，归纳了蒙特卡洛马尔可夫链（MCMC）算法。这类模型是Lo's（Ann。Stat。12：351-357，1984）核混合物模型的扩展，用于独立观测。内核表示以过去时间序列以及当前和过去潜在变量为条件的时间序列的已知分布。潜在变量是Dirichlet过程的独立样本，该过程是随机离散（几乎可以肯定）的分布。此类模型包括自回归过程的无限混合和广义自回归条件异方差（GARCH）过程的无限混合。

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《Statistics and computing》 |2011年第1期|p.69-81|共13页
作者
John W.Lau; Mike K.P. So;
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作者单位

School of Mathematics and Statistics, University of Western Australia, 35 Stirling Highway, Crawley 6009, Perth, Australia;

rnDepartment of Information Systems, Business Statistics and Operations Management, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, Hong Kong;

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bayesian nonparametric; dirichlet process prior; poisson-dirichlet process prior; bayesian poisson calculus; GARCH; volatility estimation;

机译：贝叶斯非参数Dirichlet过程先验;泊松-狄里克雷过程先验;贝叶斯泊松演算GARCH;波动率估计;

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A Monte Carlo Markov chain algorithm for a class of mixture time series models

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