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.
Arashi,M . (2023). Some Theoretical Results on the Tensor Elliptical Distribution. Journal of Data Science and Modeling, 1(2), 29-41. doi: 10.22054/jcsm.2021.47404.1019
MLA
Arashi,M . "Some Theoretical Results on the Tensor Elliptical Distribution", Journal of Data Science and Modeling, 1, 2, 2023, 29-41. doi: 10.22054/jcsm.2021.47404.1019
HARVARD
Arashi M. (2023). 'Some Theoretical Results on the Tensor Elliptical Distribution', Journal of Data Science and Modeling, 1(2), pp. 29-41. doi: 10.22054/jcsm.2021.47404.1019
CHICAGO
M Arashi, "Some Theoretical Results on the Tensor Elliptical Distribution," Journal of Data Science and Modeling, 1 2 (2023): 29-41, doi: 10.22054/jcsm.2021.47404.1019
VANCOUVER
Arashi M. Some Theoretical Results on the Tensor Elliptical Distribution. JDSM. 2023;1(2):29-41. doi: 10.22054/jcsm.2021.47404.1019
