A skew Gaussian decomposable graphical model

Hamid Zareifard, Håvard Rue, Majid Jafari Khaledi and Finn Lindgren

Journal of Multivariate Analysis, 2016, vol. 145, issue C, 58-72

Abstract: This paper proposes 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 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.

Keywords: Decomposable graphical models; Multivariate closed skew normal distribution; Conditional independence; Noninformative prior (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2015.08.011

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