Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior.
Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior.
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DOI：
10.1348/000711010x497262
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发表时间：
2011-02
期刊：
影响因子：
--
通讯作者：
Zhu H
中科院分区：
文献类型：
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作者：
Chow SM;Tang N;Yuan Y;Song X;Zhu H
时间序列和其他动态模型中的参数通常表现出复杂的范围限制，并且它们的分布可能严重偏离多元正态分布或其他标准参数分布。我们使用截断狄利克雷过程（DP）作为一个非参数先验的动态参数在一个新的非线性贝叶斯动态因子分析模型。这相当于将先验分布指定为由未知数量的离散点质量（或聚类）组成的混合分布。棒断裂先验和阻塞吉布斯采样器用于使后验样本的有效模拟。使用一系列的经验和模拟的例子，我们说明了所提出的方法在近似分布的非常不同的形状的灵活性。
Parameters in time series and other dynamic models often show complex range restrictions and their distributions may deviate substantially from multivariate normal or other standard parametric distributions. We use the truncated Dirichlet process (DP) as a non-parametric prior for such dynamic parameters in a novel nonlinear Bayesian dynamic factor analysis model. This is equivalent to specifying the prior distribution to be a mixture distribution composed of an unknown number of discrete point masses (or clusters). The stick-breaking prior and the blocked Gibbs sampler are used to enable efficient simulation of posterior samples. Using a series of empirical and simulation examples, we illustrate the flexibility of the proposed approach in approximating distributions of very diverse shapes.