The problem of variable selection for linear regression in a high dimension model is considered. A new method, called Extended-VISA (Ext-VISA), is proposed to simultaneously select variables and encourage a grouping effect where strongly correlated predictors tend to be in or out of the model...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Many present day applications of statistical learning involve large numbers of predictor variables. Often, that number is much larger than the number of cases or observations available for training the learning algorithm. In such situations, traditional methods fail. Recently, new techniques...

In this paper, we empirically assess the predictive accuracy of a large group of models based on the use of principle components and other shrinkage methods, including Bayesian model averaging and various bagging, boosting, LASSO and related methods Our results suggest that model averaging does...