The analysis of genomics alterations that may occur in nature when segments of
chromosomes are copied (known as copy number alterations) has been a focus of research …

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 …

In many applications it is of interest to determine a limited number of important explanatory
factors (representing groups of potentially overlapping predictors) rather than original …

Insights into complex, high-dimensional data can be obtained by discovering features of the
data that match or do not match a model of interest. To formalize this task, we introduce the" …

We present a Bayesian variable selection method for the setting in which the number of
independent variables or predictors in a particular dataset is much larger than the available …

The fundamental problem of gene selection via cDNA data is to identify which genes are
differentially expressed across different kinds of tissue samples (eg normal and cancer) …

High-dimensional data, where the number of features or covariates can even be larger than
the number of independent samples, are ubiquitous and are encountered on a regular basis …

Variable selection for structured covariates lying on an underlying known graph is a problem
motivated by practical applications, and has been a topic of increasing interest. However …

We consider the problem of variable selection in regression modeling in high-dimensional
spaces where there is known structure among the covariates. This is an unconventional …

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 …