A Monte Carlo Markov chain algorithm for a class of mixture time series models
A Monte Carlo Markov chain algorithm for a class of mixture time series models
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DOI：
10.1007/s11222-009-9147-6
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发表时间：
2011-01-01
影响因子：
2.2
通讯作者：
So, Mike K. P.
中科院分区：
文献类型：
--
作者：
Lau, John W.;So, Mike K. P.
针对一类Dirichlet过程上的核混合时间序列模型，在Gibbs加权中餐厅过程算法的基础上，推广了MonteCarlo Markov Chain（MCMC）算法.这类模型是Lo's（Ann. Stat. 12：351-357，1984）用于独立观测的核混合模型。内核代表了一个已知的时间序列分布的条件，过去的时间序列和现在和过去的潜变量。潜变量是来自狄利克雷过程的独立样本，狄利克雷过程是一个随机离散（几乎必然）分布。这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。
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.