Group lasso is a natural extension of lasso and selects variables in a grouped manner. However, group lasso suffers from estimation inefficiency and selection inconsistency, To remedy these problems, we propose the adaptive group lasso method. We show theoretically that the new method is able to identify the true model consistently, and the resulting estimator can be as efficient as oracle. Numerical studies confirmed our theoretical findings. (c) 2008 Elsevier B.V. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000259075900020&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Interdisciplinary Applicati...
We consider the problem of selecting grouped variable in linear regression via the group Lasso and M...
We study a group lasso estimator for the multivariate linear regression model that accounts for corr...
Abstract Penalized regression is an attractive framework for variable selection problems. Often, var...
10.1016/j.csda.2008.05.006Computational Statistics and Data Analysis52125277-5286CSDA
In regression problems where covariates can be naturally grouped, the group Lasso is an attractive m...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
We establish estimation and model selection consistency, prediction and estimation bounds and persis...
The lasso is a popular tool that can be used for variable selection and esti- mation, however, class...
We define the group lasso estimator for the natural parameters of the exponential families of distri...
Recent work has focused on the problem of conducting linear regression when the number of covariates...
17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown paramet...
Variable selection is an important property of shrinkage methods. The adaptive lasso is an oracle pr...
We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties ...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
We consider the least-square regression problem with regularization by a block!1-norm, that is, a su...
We consider the problem of selecting grouped variable in linear regression via the group Lasso and M...
We study a group lasso estimator for the multivariate linear regression model that accounts for corr...
Abstract Penalized regression is an attractive framework for variable selection problems. Often, var...
10.1016/j.csda.2008.05.006Computational Statistics and Data Analysis52125277-5286CSDA
In regression problems where covariates can be naturally grouped, the group Lasso is an attractive m...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
We establish estimation and model selection consistency, prediction and estimation bounds and persis...
The lasso is a popular tool that can be used for variable selection and esti- mation, however, class...
We define the group lasso estimator for the natural parameters of the exponential families of distri...
Recent work has focused on the problem of conducting linear regression when the number of covariates...
17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown paramet...
Variable selection is an important property of shrinkage methods. The adaptive lasso is an oracle pr...
We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties ...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
We consider the least-square regression problem with regularization by a block!1-norm, that is, a su...
We consider the problem of selecting grouped variable in linear regression via the group Lasso and M...
We study a group lasso estimator for the multivariate linear regression model that accounts for corr...
Abstract Penalized regression is an attractive framework for variable selection problems. Often, var...