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June 2011 Robust graphical modeling of gene networks using classical and alternative t-distributions
Michael Finegold, Mathias Drton
Ann. Appl. Stat. 5(2A): 1057-1080 (June 2011). DOI: 10.1214/10-AOAS410


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 multivariate t-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 computationally efficient approach to model selection in the t-distribution case. We consider two versions of multivariate t-distributions, one of which requires the use of approximation techniques. For this distribution, we describe a Markov chain Monte Carlo EM algorithm based on a Gibbs sampler as well as a simple variational approximation that makes the resulting method feasible in large problems.


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Michael Finegold. Mathias Drton. "Robust graphical modeling of gene networks using classical and alternative t-distributions." Ann. Appl. Stat. 5 (2A) 1057 - 1080, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1232.62083
MathSciNet: MR2840186
Digital Object Identifier: 10.1214/10-AOAS410

Rights: Copyright © 2011 Institute of Mathematical Statistics


Vol.5 • No. 2A • June 2011
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