Skew Gaussian Markov Random Fields Under Decomposable Graphs

Hamid Zareifard and Majid Jafari Khaledi

Environmetrics, 2025, vol. 36, issue 6

Abstract: Conditional independence and sparsity are pivotal concepts in parsimonious statistical models such as Markov random fields. Statistical modeling in this subject has been limited to the Gaussianity assumption so far, partly due to the difficulty in preserving the Markov property. As the data often exhibit non‐normality, we applied a multivariate closed skew normal distribution to introduce a novel skew Gaussian Markov random field with respect to a decomposable graph. Subsequently, after investigating the main probabilistic features of the introduced random process, we specifically focused on modeling autocorrelated data online, and thereafter, an intrinsic version of the skew Gaussian Markov random field was presented. We applied Markov chain Monte Carlo algorithms for Bayesian inference. The identifiability of the parameters was investigated using a simulation study. Finally, the usefulness of our methodology was demonstrated by analyzing two datasets.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/env.70039

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:envmet:v:36:y:2025:i:6:n:e70039

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1180-4009

Access Statistics for this article

More articles in Environmetrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().