Sparse Bayesian Grouped Regression with Application to Genome-Wide Association Studies

Abstract

Statistical variable selection, also known as feature selection, has become an indispensable tool in many research areas involving machine learning and data mining. The object of statistical variable selection is to select the best subset of predictors for fitting or predicting the response variable from a potentially large collection of candidate predictors. It is particularly important in high-dimensional problems such as cancer genetics, where there are potentially thousands of predictors and only a few are associated with the outcome. Moreover, predictors may have underlying group structures in them, so it is desirable to take the underlying group effects into consideration when performi..