Document Type : Research Manuscript

Author

• Mohammad Arashi

Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology

Abstract

The multilinear normal distribution is a widely used tool in the tensor analysis of magnetic resonance imaging (MRI). Diffusion tensor MRI provides a statistical estimate of a symmetric 2nd-order diffusion tensor for each voxel within an imaging volume. In this article, tensor elliptical (TE) distribution is introduced as an extension to the multilinear normal (MLN) distribution. Some properties, including the characteristic function and distribution of affine transformations are given. An integral representation connecting densities of TE and MLN distributions is exhibited that is used in deriving the expectation of any measurable function of a TE variate.

Keywords

• Characteristic generator
• Inverse Laplace transform
• Stochastic representation
• Tensor
• Vectorial operator

Main Subjects

• Mathematical Computing