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

Mehrdad Naderi*, Andriette Bekker, Mohammad Arashi, Ahad Jamalizadeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
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Abstract

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.
Original languageEnglish
Article numbere0230773
Number of pages20
JournalPLoS One
Volume15
Issue number4
DOIs
Publication statusPublished - 9 Apr 2020
Externally publishedYes

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