In this paper, we compare two different variable selection approaches for linear regression models: Autometrics … (automatic general-to-specific selection) and LASSO (?1-norm regularization). In a simulation study, we show the performance of …

the estimation error of the Lasso under two different sets of conditions on the covariates as well as the error terms … constants. These results are then used to show that the Lasso can be consistent in even very large models where the number of … regressors increases at an exponential rate in the sample size. Conditions under which the Lasso does not discard any relevant …

and the adaptive LASSO and penalize both the coefficient functions and their derivatives using an adaptive L1 penalty. We … give conditions under which this new adaptive LASSO consistently identifies the significant variables and parametric …

individual specific variables that all could potentially impact the retirement decision.We use variants of the Lasso and the … adaptive Lasso applied to logistic regression in order to uncover determinants of the retirement decision. To the best of our …

We study the distribution of the adaptive LASSO estimator (Zou (2006)) in finite samples as well as in the large …-sample limit. The large-sample distributions are derived both for the case where the adaptive LASSO estimator is tuned to perform …' property of the adaptive LASSO estimator established in Zou 2006). Moreover, we also provide an impossibility result regarding …

approach with locally weighted regression to achieve sparse models. Specifically, the lasso is a shrinkage and selection method … for linear regression. We present an algorithm that embeds lasso in an iterative procedure that alternatively computes … weights and performs lasso-wise regression. The algorithm is tested on three synthetic scenarios and two real data sets …