Some characteristics of tensor-variate skew-normal distribution and its application in image analysis

Tajadod, Seyed Ali Asgar; Yousefzadeh, Fatemeh; Arellano-Valle, R.B.; Jomhoori, Sara

doi:10.22103/jmmr.2025.24813.1763

Some characteristics of tensor-variate skew-normal distribution and its application in image analysis

Document Type : Research Paper

Authors

Seyed Ali Asgar Tajadod ¹

Fatemeh Yousefzadeh ¹

R.B. Arellano-Valle ²

Sara Jomhoori ¹

¹ Department of Statistics, University of Birjand, Birjand, Iran

² Department of Estadistica, Pontificia Universidad Catolica de Chile, Santiago, Chile

10.22103/jmmr.2025.24813.1763

Abstract

In recent years, due to the increasing growth of technology and new technologies, data is obtained in more complex structures as the main component in analysis. One of these complex structures is tensors. Therefore, in order to answer this need (analysis of data with tensor structure), it is necessary to expand statistical concepts and methods in the field of data with tensor structure. On the other hand, in reality, we may also encounter skew data. Therefore, in this article, we have introduced the skew normal tensor distribution and obtained some of its important statistical properties. Subsequently, we employed the EM algorithm to obtain maximum likelihood estimates of the parameters and assessed their accuracy through simulation studies. Finally, we have shown the effectiveness of the obtained estimators with real data.

Keywords

Kronecker-separable covariance

Multidimensional array

Tensor

Skew distributions

EM algorithm

Subjects

Applied Mathematics

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