Add to ORKGPhillips (J. Multivariate Anal. 16 (1985) 157) generalizes Cramer's (Mathematical Methods of Statistics, Princeton University Press, Princeton, NJ, 1946) inversion formula for the distribution of a quotient of two scalar random variables to the matrix quotient case. However, he gives the result for the asymmetric matrix quotient case. This note extends Phillips' (1985) result to the symmetric matrix quotient case.Matrix variate Positive definite Density Transformation Moment generating function Inversion formula
A unified framework is established for the study of the computation of the distribution function fro...
Moments of multivariate and matrix-variate distributions are obtained for both complex and real case...
Let $ S_1\sim W_k(n_1;\boldsymbol\Sigma)$ and $ S_2\sim W_k(n_2,\boldsymbol\Sigma)$ be independent W...
AbstractPhillips (J. Multivariate Anal. 16 (1985) 157) generalizes Cramer's (Mathematical Methods of...
Cramér's inversion formula for the distribution of a quotient is generalized to matrix variates and ...
Cramér’s inversion formula for the distribution of a quotient is generalized to matrix variates and ...
[[abstract]]Since Azzalini (1985,1986) introduced the univariate skew-normal distribution, there are...
AbstractThe asymptotic distributions of the elementary symmetric functions (esf's) of the characteri...
AbstractLet the column vectors of X: p × n be distributed as independent normals with the same covar...
AbstractThe Moore–Penrose inverse of a singular or nonsquare matrix is not only existent but also un...
Inversion formulae are derived that express the density and distribution function of a ratio of rand...
This thesis develops a skewing methodology for the formulation of two-piece families of distri- buti...
In 2007, Domínguez-Molina et al. obtained the moment generating function (mgf) of the matrix variate...
If the univariate random variable X follows the distribution with distribution function F, then so d...
AbstractThe (univariate) t-distribution and symmetric V.G. distribution are competing models [D.S. M...
A unified framework is established for the study of the computation of the distribution function fro...
Moments of multivariate and matrix-variate distributions are obtained for both complex and real case...
Let $ S_1\sim W_k(n_1;\boldsymbol\Sigma)$ and $ S_2\sim W_k(n_2,\boldsymbol\Sigma)$ be independent W...
AbstractPhillips (J. Multivariate Anal. 16 (1985) 157) generalizes Cramer's (Mathematical Methods of...
Cramér's inversion formula for the distribution of a quotient is generalized to matrix variates and ...
Cramér’s inversion formula for the distribution of a quotient is generalized to matrix variates and ...
[[abstract]]Since Azzalini (1985,1986) introduced the univariate skew-normal distribution, there are...
AbstractThe asymptotic distributions of the elementary symmetric functions (esf's) of the characteri...
AbstractLet the column vectors of X: p × n be distributed as independent normals with the same covar...
AbstractThe Moore–Penrose inverse of a singular or nonsquare matrix is not only existent but also un...
Inversion formulae are derived that express the density and distribution function of a ratio of rand...
This thesis develops a skewing methodology for the formulation of two-piece families of distri- buti...
In 2007, Domínguez-Molina et al. obtained the moment generating function (mgf) of the matrix variate...
If the univariate random variable X follows the distribution with distribution function F, then so d...
AbstractThe (univariate) t-distribution and symmetric V.G. distribution are competing models [D.S. M...
A unified framework is established for the study of the computation of the distribution function fro...
Moments of multivariate and matrix-variate distributions are obtained for both complex and real case...
Let $ S_1\sim W_k(n_1;\boldsymbol\Sigma)$ and $ S_2\sim W_k(n_2,\boldsymbol\Sigma)$ be independent W...