Heavy-tailed matrix-variate hidden Markov models
将隐马尔可夫模型扩展到矩阵变量框架，使用t分布和污染正态分布处理重尾数据，改进聚类和异常检测，并通过R包实现参数估计，应用于意大利各省劳动力市场动态分析。
The matrix-variate framework for hidden Markov models (HMMs) is expanded with two families of models using matrix-variate t and contaminated normal distributions . These models improve the handling of tail behavior, clustering, and address challenges in identifying outlying matrices in matrix-variate data. Two Expectation-Conditional Maximization (ECM) algorithms are implemented in the R package MatrixHMM for parameter estimation. Simulations assess parameter recovery, robustness, anomaly detection , and show the advantages over alternative approaches. The models are applied to real-world data to analyze labor market dynamics across Italian provinces.