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Hidden Markov models (HMMs) emerge as a powerful tool for examining various time-dependent data structures. Within the context of matrix-variate longitudinal data, the literature has seen notable progress in recent years. In this study, we apply a set of parsimonious matrix-variate HMMs to a dataset that records crime indicators across 106 provinces in Italy. Utilizing the flexibility of HMMs and matrix-variate structure, various states are identified, and the transitions of provinces between these states are examined over time.

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Analyzing Italian Crime Data Using Matrix-Variate Hidden Markov Models

  • 会议论文

  • 发表日期：

    2025年

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  • Salvatore D. Tomarchio,
  • Antonio Punzo,
  • Antonello Maruotti

摘要

Hidden Markov models (HMMs) emerge as a powerful tool for examining various time-dependent data structures. Within the context of matrix-variate longitudinal data, the literature has seen notable progress in recent years. In this study, we apply a set of parsimonious matrix-variate HMMs to a dataset that records crime indicators across 106 provinces in Italy. Utilizing the flexibility of HMMs and matrix-variate structure, various states are identified, and the transitions of provinces between these states are examined over time.