Heavy-tailed matrix-variate hidden Markov models

Salvatore D. Tomarchio

Computational Statistics & Data Analysis, 2025, vol. 211, issue C

Abstract: 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.

Keywords: Atypical data; Heavy-tails; Hidden Markov models; Matrix-variate (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:211:y:2025:i:c:s016794732500074x

DOI: 10.1016/j.csda.2025.108198

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