Univariate likelihood projections and characterizations of the multivariate normal distribution

Characterizations of Multivariate Normal-Poisson Model

‎Multivariate normal-Poisson model has been recently introduced as a special case of normal stable Tweedie models‎. ‎The model is composed of a univariate Poisson variable‎, ‎and the remaining variables given the Poisson one are independent Gaussian variables with variance the value of the Poisson component‎. ‎Two characterizations of this model are shown‎, ‎...

Univariate and multivariate Chen-Stein characterizations – a parametric approach

We provide a general framework for characterizing families of (univariate, multivariate, discrete and continuous) distributions in terms of a parameter of interest. We show how this allows for recovering known Chen-Stein characterizations, and for constructing many more. Several examples are worked out in full, and different potential applications are discussed. AMS 2000 subject classifications...

A Few Characterizations of the Univariate Continuous Distributions

Various characterizations of distributions, in their generality, are presented in terms of the conditional expectations. Some special  examples are given as well.

Screening for a Multivariate Mixture Normal Distribution

Penalized Maximum Likelihood Estimation for Univariate Normal Mixture Distributions

Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM alg...