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.
Original language | English (US) |
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Pages (from-to) | 58-72 |
Number of pages | 15 |
Journal | JOURNAL OF MULTIVARIATE ANALYSIS |
Volume | 145 |
DOIs | |
State | Published - Mar 1 2016 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015 Elsevier Inc.
Keywords
- Conditional independence
- Decomposable graphical models
- Multivariate closed skew normal distribution
- Noninformative prior
ASJC Scopus subject areas
- Statistics and Probability
- Numerical Analysis
- Statistics, Probability and Uncertainty