Robust Gaussian graphical modeling via l1 penalization.

ABSTRACT: Gaussian graphical models have been widely used as an effective method for studying the conditional independency structure among genes and for constructing genetic networks. However, gene expression data typically have heavier tails or more outlying observations than the standard Gaussian distribution. Such outliers in gene expression data can lead to wrong inference on the dependency structure among the genes. We propose a l(1) penalized estimation procedure for the sparse Gaussian graphical models that is robustified against possible outliers. The likelihood function is weighted according to how the observation is deviated, where the deviation of the observation is measured based on its own likelihood. An efficient computational algorithm based on the coordinate gradient descent method i

SUBMITTER: Sun H 

PROVIDER: S-EPMC3535542 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature