A theoretical framework for Landsat data modeling based on the matrix variate mean-mixture of normal model

• Published in: PloS one Vol. 15; no. 4; p. e0230773
• Main Authors: Naderi, Mehrdad, Bekker, Andriette, Arashi, Mohammad, Jamalizadeh, Ahad
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09-04-2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Characteristic functions; Computer simulation; Control theory; Ecology and Environmental Sciences; Geospatial data; Landsat; Landsat satellites; Matrix methods; Maximum likelihood estimates; Normal distribution; Parameter estimation; Physical Sciences; Quadratic forms; Remote sensing; Research and Analysis Methods; Satellites (Spacecraft); Skewness; Stochasticity; Iran; South Africa
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0230773

This paper introduces a new family of matrix variate distributions based on the mean-mixture of normal (MMN) models. The properties of the new matrix variate family, namely stochastic representation, moments and characteristic function, linear and quadratic forms as well as marginal and conditional distributions are investigated. Three special cases including the restricted skew-normal, exponentiated MMN and the mixed-Weibull MMN matrix variate distributions are presented and studied. Based on the specific presentation of the proposed model, an EM-type algorithm can be directly implemented for obtaining maximum likelihood estimate of the parameters. The usefulness and practical utility of the proposed methodology are illustrated through two conducted simulation studies and through the Landsat satellite dataset analysis.