论文标题
与隐藏结构的串行相关多元数据的副群体建模
Copula Modelling of Serially Correlated Multivariate Data with Hidden Structures
论文作者
论文摘要
我们提出了一个基于Copula Markov模型（HMM）的基于Copula的扩展，该扩展适用于样本中每次记录的观测值是多元的。 Copula扩展产生的联合模型允许根据来自多个观察结果的信息来解码隐藏状态。但是，与独立边缘的情况不同，嵌入在可能性中的copula依赖性结构会带来其他计算挑战。我们使用在边缘的推理函数框架内开发的EM算法的理论变化来解决后者。我们使用数值实验和房屋入住分析说明了该方法。
We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a theoretically-justified variation of the EM algorithm developed within the framework of inference functions for margins. We illustrate the method using numerical experiments and an analysis of house occupancy.
