A selective review of robust variable selection with applications in bioinformatics.

ABSTRACT: A drastic amount of data have been and are being generated in bioinformatics studies. In the analysis of such data, the standard modeling approaches can be challenged by the heavy-tailed errors and outliers in response variables, the contamination in predictors (which may be caused by, for instance, technical problems in microarray gene expression studies), model mis-specification and others. Robust methods are needed to tackle these challenges. When there are a large number of predictors, variable selection can be as important as estimation. As a generic variable selection and regularization tool, penalization has been extensively adopted. In this article, we provide a selective review of robust penalized variable selection approaches especially designed for high-dimensional data from bio

SUBMITTER: Wu C 

PROVIDER: S-EPMC4570200 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature