TY - JOUR
T1 - A theoretical framework for Landsat data modeling based on the matrix variate mean-mixture of normal model
AU - Naderi, Mehrdad
AU - Bekker, Andriette
AU - Arashi, Mohammad
AU - Jamalizadeh, Ahad
N1 - Funding information: 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 9701947.
PY - 2020/4/9
Y1 - 2020/4/9
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85083002634
U2 - 10.1371/journal.pone.0230773
DO - 10.1371/journal.pone.0230773
M3 - Article
SN - 1932-6203
VL - 15
JO - PLoS One
JF - PLoS One
IS - 4
M1 - e0230773
ER -