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<p>Matrix-variate distributions are powerful tools for modeling three-way datasets that often arise in longitudinal and multidimensional spatio-temporal studies. However, observations in these datasets can be missing or subject to some detection limits because of the restriction of the experimental apparatus. Here, we develop an efficient EM-type algorithm for maximum likelihood estimation of parameters, in the context of interval-censored and/or missing data, utilizing the matrix-variate normal distribution. This algorithm provides closed-form expressions that rely on truncated moments, offering a reliable approach to parameter estimation under these conditions. Results obtained from the analysis of both simulated data and real case studies concerning water quality monitoring are reported to demonstrate the effectiveness of the proposed method.</p>

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An EM algorithm for fitting matrix-variate normal distributions on interval-censored and missing data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月28日

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  • Victor H. Lachos,
  • Salvatore D. Tomarchio,
  • Antonio Punzo,
  • Salvatore Ingrassia

摘要

Matrix-variate distributions are powerful tools for modeling three-way datasets that often arise in longitudinal and multidimensional spatio-temporal studies. However, observations in these datasets can be missing or subject to some detection limits because of the restriction of the experimental apparatus. Here, we develop an efficient EM-type algorithm for maximum likelihood estimation of parameters, in the context of interval-censored and/or missing data, utilizing the matrix-variate normal distribution. This algorithm provides closed-form expressions that rely on truncated moments, offering a reliable approach to parameter estimation under these conditions. Results obtained from the analysis of both simulated data and real case studies concerning water quality monitoring are reported to demonstrate the effectiveness of the proposed method.