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

• Published in: Statistics and computing Vol. 35; no. 2
• Main Authors: Lachos, Victor H., Tomarchio, Salvatore D., Punzo, Antonio, Ingrassia, Salvatore
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2025; Springer Nature B.V
• Subjects: 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; Censored data; Missing data; ECM algorithm; Truncated moments; Matrix-variate distribution

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...