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 …

selected by first-step penalized estimators, typically LASSO. It is well known that LASSO can estimate the regression function … at nearly the oracle rate, and is thus hard to improve upon. We show that post-LASSO performs at least as well as LASSO … the LASSO-based model selection fails” in the sense of missing some components of the true” regression model. By the true …

approached by means of Bayesian model averaging, using the Bayesian interpretation of the LASSO. This is used to generate a set … posterior may turn out to be "thinner" than desired, and bootstrapping of the LASSO is used to increase this population. This …

continuum of target parameters is of interest and the Lasso-type or post-Lasso type methods are used to estimate a continuum of … establish rate and consistency results for continua of Lasso or post-Lasso type methods for estimating continua of the (nuisance …

for a continuum of target parameters and for Lasso-type or Post-Lasso type methods to be used as estimators of a continuum … continua of Lasso or Post-Lasso type estimators for continua of (nuisance) regression functions and provide practical …

autoregressive models. We propose using Lasso-type estimators to reduce the dimensionality to a manageable one and provide strong …