Use of microarray technology often leads to high-dimensional and low-sample size (HDLSS) data settings. A variety of … of microarray data obtained from a set of patients with diffuse large B-cell lymphoma where time to survival is of …

popular approaches in this research field is given by Lasso-type methods. An alternative approach is based on information … criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this …

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso …, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the … (implemented in lasso2), K-fold cross-validation and h-step ahead rolling cross-validation for cross-section, panel and time …

Summary This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and … heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In … particular, inconsistent results are obtained for pairwise highly correlated predictors. An alternative to the Lasso is …

We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top …

We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top …

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso …, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the … (implemented in lasso2), K-fold cross-validation and h-step ahead rolling cross-validation for cross-section, panel and time …