Multivariate lifetime distributions for the exponential dispersion family

Multivariate matrix-exponential distributions

In this extended abstract we define a class of distributions which we shall refer to as multivariate matrix–exponential distributions (MVME). They are defined in a natural way, inspired by the definition of univariate matrix– exponential distributions, as the distributions on R+ having a rational (multidimensional) Laplace transform. A multidimensional rational function is the fraction between ...

Mixtures of multivariate power exponential distributions.

An expanded family of mixtures of multivariate power exponential distributions is introduced. While fitting heavy-tails and skewness have received much attention in the model-based clustering literature recently, we investigate the use of a distribution that can deal with both varying tail-weight and peakedness of data. A family of parsimonious models is proposed using an eigen-decomposition of...

A new family of compound lifetime distributions

Hessian Stochastic Ordering in the Family of multivariate Generalized Hyperbolic Distributions and its Applications

In this paper, random vectors following the multivariate generalized hyperbolic (GH) distribution are compared using the hessian stochastic order. This family includes the classes of symmetric and asymmetric distributions by which different behaviors of kurtosis in skewed and heavy tail data can be captured. By considering some closed convex cones and their duals, we derive some necessary and s...

Graphical models via univariate exponential family distributions

Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a gene...