Copula Modeling of Serially Correlated Multivariate Data with Hidden Structures

• 成果类型: Article
• 署名作者: Zimmerman, Robert; Craiu, Radu V.; Leos-Barajas, Vianey
• 署名单位: University of Toronto; University of Toronto
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2023.2263202
• 发表日期: 2024
• 页码: 2598-2609
• 关键词: occupancy detection; maximum-likelihood; high dimensions; markov-models; algorithm

摘要

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 room occupancy. Supplementary materials for this article are available online.