@Article{CMR-39-3,
author = {Liu, Binghui and Jianhua, Guo},
title = {Decomposition of Covariate-Dependent Graphical Models with Categorical Data},
journal = {Communications in Mathematical Research },
year = {2023},
volume = {39},
number = {3},
pages = {414--436},
abstract = {<p style="text-align: justify;">Graphical models are wildly used to describe conditional dependence relationships among interacting random variables. Among statistical
inference problems of a graphical model, one particular interest is utilizing its
interaction structure to reduce model complexity. As an important approach
to utilizing structural information, decomposition allows a statistical inference
problem to be divided into some sub-problems with lower complexities. In this
paper, to investigate decomposition of covariate-dependent graphical models,
we propose some useful definitions of decomposition of covariate-dependent
graphical models with categorical data in the form of contingency tables. Based
on such a decomposition, a covariate-dependent graphical model can be split
into some sub-models, and the maximum likelihood estimation of this model
can be factorized into the maximum likelihood estimations of the sub-models.
Moreover, some sufficient and necessary conditions of the proposed definitions
of decomposition are studied.</p>},
issn = {2707-8523},
doi = {https://doi.org/10.4208/cmr.2022-0030},
url = {https://global-sci.com/article/81466/decomposition-of-covariate-dependent-graphical-models-with-categorical-data}
}