avoiding the overshrinkage of a Lasso-type estimator. It combines the idea of the VISA algorithm which avoids the overshrinkage … problem of regression coefficients and those of the Lasso-type estimators, based on ℓ1+ℓ2 penalty, that overcome the … limitation of the grouping effect of Lasso in high dimension. It is based on a modified VISA algorithm, so it is also …

with Bayesian Markov Chain Monte Carlo. The resulting model is equivalent to the frequentist lasso procedure. A … covariates is provided by the approach. An implementation of the lasso procedure for binary quantile regression models is …

We propose the Bayesian adaptive Lasso (BaLasso) for variable selection and coefficient estimation in linear regression … hierarchical Bayesian interpretation of the Lasso. Our formulation also permits prediction using a model averaging strategy. We …

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO … in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when … excluded. Next, non-asymptotic probabilities are given for the Adaptive LASSO to select the correct sign pattern (and hence the …

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 …

selection operator (LASSO), this approach sets certain regression coefficients exactly to zero, thus performing variable … selection. However, such a framework, contrary to the LASSO, has never been used in regression models for survival data with …

(automatic general-to-specific selection) and LASSO (?1-norm regularization). In a simulation study, we show the performance of …