In this paper, we are concerned with regression problems where covariates can be grouped in nonoverlapping blocks, and where only a few of them are assumed to be active. In such a situation, the group Lasso is an attractive method for variable selection since it promotes sparsity of the groups. We study the sensitivity of any group Lasso solution to the observations and provide its precise loca...

According to the article[2], we present a new method for post-selection inference for l1(lasso)penalized likelihood models, including generalized regression models. Our approach generalizes the post-selection framework presented in Lee et al. (2013)[1]. The method provides P-values and confidence intervals that are asymptotically valid, conditional on the inherent selection done by the lasso. W...

BACKGROUND The study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear ...

We congratulate the authors for an interesting article and an innovative proposal to testing the significance of the predictor variables selected by the Lasso. There is much material for thought and exploration. Research on high-dimensional regression has been very active in recent years, but most of the efforts have so far focused on estimation. Despite the popularity of the Lasso as a variabl...

The Dantzig selector (Candes and Tao, 2007) is a new approach that has been proposed for performing variable selection and model fitting on linear regression models. It uses an L1 penalty to shrink the regression coefficients towards zero, in a similar fashion to the Lasso. While both the Lasso and Dantzig selector potentially do a good job of selecting the correct variables, several researcher...

In generalized linear regression problems with an abundant number of features, lasso-type regularization which imposes an `-constraint on the regression coefficients has become a widely established technique. Crucial deficiencies of the lasso were unmasked when Zhou and Hastie (2005) introduced the elastic net. In this paper, we propose to extend the elastic net by admitting general nonnegative...

As a flexible alternative to the Cox model, the additive risk model assumes that the hazard function is the sum of the baseline hazard and a regression function of covariates. For right censored survival data when variable selection is needed along with model estimation, we propose a path consistent model selector using a modified Lasso approach, under the additive risk model assumption. We sho...

Multiple imputation (MI) is a commonly used technique for handling missing data in large-scale medical and public health studies. However, variable selection on multiply-imputed data remains an important and longstanding statistical problem. If a variable selection method is applied to each imputed dataset separately, it may select different variables for different imputed datasets, which makes...