Matrix variate slash distribution

Abstract

Please cite this article as: Y.M. Bulut, O. Arslan, Matrix variate slash distribution, Journal of Multivariate Analysis (2015), http://dx. AbstractIn this paper, we introduce a matrix variate slash distribution as a scale mixture of the matrix variate normal and the uniform distributions. We study some properties of the proposed distribution and give maximum likelihood (ML) estimators of its parameters using EM algorithm. We provide an iteratively reweighting algorithm to compute the ML estimates. Also, we give a small simulation study to show performance of the algorithm.

Joint Mixability of Elliptical Distributions and Related Families

“…We denote this as X ∼ SE p (µ, Σ, ψ; q). Properties of this family are discussed in Gómez, Quintana and Torres (2007) and Bulut and Arslan (2015).…”

Nuclear-L1 norm joint regression for face reconstruction and recognition with mixed noise

“…Subsequently, Reyes et al [36] introduced the modified slash distribution, and provided two illustrations with real data showing that the new distribution fits better the data than the ordinary slash distribution. More recently, Bulut and Arslan [37] presented Matrix variate slash distribution as a scale mixture of the matrix variate normal and the uniform distributions, and provided a procedure to obtain the ML estimators for the parameters of the proposed distribution. In the following, we will give the definitions of the generalized multivariate version and matrix version of slash distribution.…”

“…It is easy to see that matrix variate Slash distribution in [37] is a special case of Definition 2. As we know, visualizing matrix variate distribution is difficult because of the limitation of dimension in the real space.…”

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

“…To deal with the heavy tailed data, Kshirsagar and Bartlett [12] proposed the matrix variate t distribution by showing that the estimator of the parameter matrix of regression coefficients unconditionally follows matrix variate t model. Bulut and Arslan [13] proposed the matrix variate slash distribution as a scale mixture of the matrix variate normal and the uniform distributions. Moreover, in accommodating skewness and kurtosis, the interest of skew distributions provides a platform for robust extension of matrix variate distribution.…”