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

doi:10.1371/journal.pone.0230773

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

PLoS One. 2020 Apr 9;15(4):e0230773. doi: 10.1371/journal.pone.0230773. eCollection 2020.

Authors

Mehrdad Naderi¹, Andriette Bekker¹, Mohammad Arashi^{1

2}, Ahad Jamalizadeh³

Affiliations

¹ Department of Statistics, Faculty of Natural & Agricultural Sciences, University of Pretoria, Pretoria, South Africa.
² Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood, Iran.
³ Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Likelihood Functions
Models, Theoretical*
Satellite Imagery / methods
Satellite Imagery / statistics & numerical data*
Statistical Distributions

Grants and funding

M. Naderi and A. Bekker acknowledge the research support provided by the National Research Foundation (NRF) of South Africa, Reference: CPRR160403161466 grant Number: 105840, Reference: SRUG190308422768 grant Number: 120839 and STATOMET. M. Arashi is also based upon research supported in part by the NRF of South Africa, Ref: IFR170227223754 grant Number: 109214 and SARChI Research Chair-UID: 71199 and Iran National Science Foundation (INSF) with grant number 97019472.