Variable selection has been the focus of much research in recent years. Bayesian methods
have found many successful applications, particularly in situations where the amount of …

Selection of significant genes via expression patterns is an important problem in microarray
experiments. Owing to small sample size and the large number of variables (genes), the …

In many clinical studies, researchers are interested in parsimonious models that
simultaneously achieve consistent variable selection and optimal prediction. The resulting …

The objective of finding a parsimonious representation of the observed data by a statistical
model that is also capable of accurate prediction is commonplace in all domains of statistical …

In large-scale genomic applications vast numbers of molecular features are scanned in
order to find a small number of candidates which are linked to a particular disease or …

The ordinary Bayesian information criterion is too liberal for model selection when the model
space is large. In this paper, we re-examine the Bayesian paradigm for model selection and …

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 …

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 …

Efficient variable selection in high dimensional cancer genomic studies is critical for
discovering genes associated with specific cancer types and for predicting response 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 …