Add to ORKGWe propose a new non-convex penalty in linear regression models. The new penalty function can be considered a competitor of the LASSO, SCAD or MCP penalties, as it guarantees sparse variable selection while reducing bias for the non-null estimates. We introduce the methodology and present some comparisons among different approaches
Model selection in nonparametric and semiparametric regression is of both theoretical and practical ...
This article focuses on variable selection for partially linear models when the covariates are measu...
Variable selection, Median regression, Least absolute deviations, Lasso, Perturbation, Bayesian info...
We propose a new SCAD-type penalty in general regression models. The new penalty can be considered a...
International audienceWe consider the problem of variable selection via penalized likelihood using n...
Selection of variables and estimation of regression coefficients in datasets with the number of vari...
This article proposes a variable selection method termed “subtle uprooting” for linear regression. I...
Since the proposal of the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996)...
Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potenti...
Mixed-effect models are very popular for analyzing data with a hierarchical structure. In medical ap...
The performances of penalized least squares approaches profoundly depend on the selection of the tun...
After its inception in Koenker and Bassett (1978), quantile regression has become an important and w...
There is an emerging need to advance linear mixed model technology to include variable selection met...
The use of regularization, or penalization, has become increasingly common in highdimensional statis...
We consider the problem of model (or variable) selection in the classical regression model based on ...
Model selection in nonparametric and semiparametric regression is of both theoretical and practical ...
This article focuses on variable selection for partially linear models when the covariates are measu...
Variable selection, Median regression, Least absolute deviations, Lasso, Perturbation, Bayesian info...
We propose a new SCAD-type penalty in general regression models. The new penalty can be considered a...
International audienceWe consider the problem of variable selection via penalized likelihood using n...
Selection of variables and estimation of regression coefficients in datasets with the number of vari...
This article proposes a variable selection method termed “subtle uprooting” for linear regression. I...
Since the proposal of the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996)...
Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potenti...
Mixed-effect models are very popular for analyzing data with a hierarchical structure. In medical ap...
The performances of penalized least squares approaches profoundly depend on the selection of the tun...
After its inception in Koenker and Bassett (1978), quantile regression has become an important and w...
There is an emerging need to advance linear mixed model technology to include variable selection met...
The use of regularization, or penalization, has become increasingly common in highdimensional statis...
We consider the problem of model (or variable) selection in the classical regression model based on ...
Model selection in nonparametric and semiparametric regression is of both theoretical and practical ...
This article focuses on variable selection for partially linear models when the covariates are measu...
Variable selection, Median regression, Least absolute deviations, Lasso, Perturbation, Bayesian info...