Background An important question in the analysis of biochemical data is that of identifying
subsets of molecular variables that may jointly influence a biological response. Statistical …

Grouping structures arise naturally in many statistical modeling problems. Several methods
have been proposed for variable selection that respect grouping structure in variables …

In the postgenome era, multiple types of molecular data for the same set of samples are
often available and should be analyzed jointly in an integrative analysis in order to maximize …

The presented work is motivated by the need of reliably estimating and predicting the
survival rates for individuals diagnosed with cancer, when gene expression profiles are …

It is well-established that the development of a disease, especially cancer, is a complex
process that results from the joint effects of multiple genes involved in various molecular …

Motivation In genomic studies, identifying biomarkers associated with a variable of interest is
a major concern in biomedical research. Regularized approaches are classically used to …

DNA microarrays can provide insight into genetic changes that characterize different stages
of a disease process. Accurate identification of these changes has significant therapeutic …

The lasso is one of the most commonly used methods for high-dimensional regression, but
can be unstable and lacks satisfactory asymptotic properties for variable selection. We …

Many data sets consist of variables with an inherent group structure. The problem of group
selection has been well studied, but in this paper, we seek to do the opposite: our goal is to …

The main purpose of microarray studies is screening to identify differentially expressed
genes as candidates for further investigation. Because of limited resources in this stage …