An EM algorithm for fitting matrix-variate normal distributions on interval-censored and missing data
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 app...
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| Published in | Statistics and computing Vol. 35; no. 2 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.04.2025
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0960-3174 1573-1375 1573-1375 |
| DOI | 10.1007/s11222-025-10575-0 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 39 |
| Author | Lachos, Victor H. Ingrassia, Salvatore Punzo, Antonio Tomarchio, Salvatore D. |
| Author_xml | – sequence: 1 givenname: Victor H. orcidid: 0000-0002-7239-2459 surname: Lachos fullname: Lachos, Victor H. organization: Department of Statistics, University of Connecticut – sequence: 2 givenname: Salvatore D. orcidid: 0000-0003-2690-8546 surname: Tomarchio fullname: Tomarchio, Salvatore D. email: daniele.tomarchio@unict.it organization: Department of Economics and Business, University of Catania – sequence: 3 givenname: Antonio orcidid: 0000-0001-7742-1821 surname: Punzo fullname: Punzo, Antonio organization: Department of Economics and Business, University of Catania – sequence: 4 givenname: Salvatore orcidid: 0000-0003-2052-4226 surname: Ingrassia fullname: Ingrassia, Salvatore organization: Department of Economics and Business, University of Catania |
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| Keywords | Censored data Missing data ECM algorithm Truncated moments Matrix-variate distribution |
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| SubjectTerms | Algorithms Artificial Intelligence Computer Science Datasets Maximum likelihood estimation Missing data Normal distribution Original Paper Parameter estimation Probability and Statistics in Computer Science Spatiotemporal data Statistical Theory and Methods Statistics and Computing/Statistics Programs Water quality |
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| Title | An EM algorithm for fitting matrix-variate normal distributions on interval-censored and missing data |
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