For a scale mixture of normal vector, X = A1/2G, where X G ∈ Rnand A is a positive variable, independent of the normal vector G, we obtain that the conditional variance covariance, Cov(X2 X1), is always finite a,s for m ≥ 2, where X1∈ Rnand m < n, and remains a.s. finite even for m = 1, if and only if the square root moment of the scale factor is finite. It is shown that the variance is not degenerate as in the Gaussian case, but depends upon a function SA,m(.) for which various properties are derived. Application to a uniform and stable scale of normal distributions are also give
Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for...
AbstractLet (X1, X2) be a symmetric α-stable random vector with 0 < α < 2. Its distribution is chara...
International audienceThe covariation is one of the possible dependence measures for variables where...
AbstractFor a scale mixture of normal vector, X=A1/2G, where X, G∈Rn and A is a positive variable, i...
AbstractFor a scale mixture of normal vector, X=A1/2G, where X, G∈Rn and A is a positive variable, i...
with 1 < a < 2 and spectral measure r, we find a necessary and sufficient condition in terms o...
AbstractIf X1 and X2 are independent and identically distributed (i. i. d.) with finite variance, th...
Abstract: A finite mixture of normal distributions in both mean and variance pa-rameters is a typica...
AbstractJointly α-stable random variables with index 0 < α < 2 have only finite moments of order les...
In this note we prove a novel characterization result stating that any distribution is determined un...
In this note we prove a novel characterization result stating that any distribution is determined un...
In this note we prove a novel characterization result stating that any distribution is determined un...
In this note we prove a novel characterization result stating that any distribution is determined un...
AbstractIt is shown that the conditional probability density function of Y1 given (1n) Σi=1n Yi=1Yit...
AbstractWe give necessary and sufficient conditions for the linearity of multiple regression on a ge...
Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for...
AbstractLet (X1, X2) be a symmetric α-stable random vector with 0 < α < 2. Its distribution is chara...
International audienceThe covariation is one of the possible dependence measures for variables where...
AbstractFor a scale mixture of normal vector, X=A1/2G, where X, G∈Rn and A is a positive variable, i...
AbstractFor a scale mixture of normal vector, X=A1/2G, where X, G∈Rn and A is a positive variable, i...
with 1 < a < 2 and spectral measure r, we find a necessary and sufficient condition in terms o...
AbstractIf X1 and X2 are independent and identically distributed (i. i. d.) with finite variance, th...
Abstract: A finite mixture of normal distributions in both mean and variance pa-rameters is a typica...
AbstractJointly α-stable random variables with index 0 < α < 2 have only finite moments of order les...
In this note we prove a novel characterization result stating that any distribution is determined un...
In this note we prove a novel characterization result stating that any distribution is determined un...
In this note we prove a novel characterization result stating that any distribution is determined un...
In this note we prove a novel characterization result stating that any distribution is determined un...
AbstractIt is shown that the conditional probability density function of Y1 given (1n) Σi=1n Yi=1Yit...
AbstractWe give necessary and sufficient conditions for the linearity of multiple regression on a ge...
Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for...
AbstractLet (X1, X2) be a symmetric α-stable random vector with 0 < α < 2. Its distribution is chara...
International audienceThe covariation is one of the possible dependence measures for variables where...