Penalized composite likelihood for colored graphical Gaussian models

04/03/2020
by   Qiong Li, et al.
0

This paper proposes a penalized composite likelihood method for model selection in colored graphical Gaussian models. The method provides a sparse and symmetry-constrained estimator of the precision matrix, and thus conducts model selection and precision matrix estimation simultaneously. In particular, the method uses penalty terms to constrain the elements of the precision matrix, which enables us to transform the model selection problem into a constrained optimization problem. Further, computer experiments are conducted to illustrate the performance of the proposed new methodology. It is shown that the proposed method performs well in both the selection of nonzero elements in the precision matrix and the identification of symmetry structures in graphical models. The feasibility and potential clinical application of the proposed method are demonstrated on a microarray gene expression data set.

READ FULL TEXT
research
06/06/2019

Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization

We address the task of identifying densely connected subsets of multivar...
research
08/09/2014

Robust Graphical Modeling with t-Distributions

Graphical Gaussian models have proven to be useful tools for exploring n...
research
01/28/2022

Family-wise error rate control in Gaussian graphical model selection via Distributionally Robust Optimization

Recently, a special case of precision matrix estimation based on a distr...
research
07/20/2021

Sparse composite likelihood selection

Composite likelihood has shown promise in settings where the number of p...
research
05/06/2018

Bayesian Regularization for Graphical Models with Unequal Shrinkage

We consider a Bayesian framework for estimating a high-dimensional spars...
research
04/07/2020

Model selection in the space of Gaussian models invariant by symmetry

We consider multivariate centred Gaussian models for the random variable...
research
02/19/2019

Penalized basis models for very large spatial datasets

Many modern spatial models express the stochastic variation component as...

Please sign up or login with your details

Forgot password? Click here to reset