Copula Modeling of Serially Correlated Multivariate Data with Hidden Structures
具有隐含结构的序列相关多变量数据 Copula 建模
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…