A skew Gaussian decomposable graphical model
Methodology
2013-09-23 v1 Statistics Theory
Statistics Theory
Abstract
This paper propose a novel decomposable graphical model to accommodate skew Gaussian graphical models. We encode conditional independence structure among the components of the multivariate closed skew normal random vector by means of a decomposable graph and so that the pattern of zero off-diagonal elements in the precision matrix corresponds to the missing edges of the given graph. We present conditions that guarantee the propriety of the posterior distributions under the standard noninformative priors for mean vector and precision matrix, and a proper prior for skewness parameter. The identifiability of the parameters is investigated by a simulation study. Finally, we apply our methodology to two data sets.
Cite
@article{arxiv.1309.5192,
title = {A skew Gaussian decomposable graphical model},
author = {Hamid Zareifard and Havard Rue and Majid Jafari Khaledi and Finn Lindgren},
journal= {arXiv preprint arXiv:1309.5192},
year = {2013}
}