Robust Graphical Modeling with t-Distributions
This work addresses the need for more robust graphical modeling in applications like gene expression studies, representing an incremental improvement over existing Gaussian-based methods.
The paper tackles the problem of robust inference of graphical models by advocating for multivariate t-distributions, demonstrating that penalized likelihood inference with an EM algorithm provides a computationally efficient approach for model selection.
Graphical Gaussian models have proven to be useful tools for exploring network structures based on multivariate data. Applications to studies of gene expression have generated substantial interest in these models, and resulting recent progress includes the development of fitting methodology involving penalization of the likelihood function. In this paper we advocate the use of the multivariate t and related distributions for more robust inference of graphs. In particular, we demonstrate that penalized likelihood inference combined with an application of the EM algorithm provides a simple and computationally efficient approach to model selection in the t-distribution case.