17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown parameters by a procedure inspired from the Group Lasso estimator introduced by Yuan and Lin (2006). We show that this estimator satisfies a sparsity oracle inequality, i.e., a bound in terms of the number of non-zero components of the oracle vector. We prove that this bound is better, in some cases, than the one achieved by the Lasso and the Dantzig selector
In this paper, we propose an algorithm encouraging group sparsity under some convex constraint. It s...
We consider the problem of estimating a function f0 in logistic regression model. We propose to esti...
During the last few years, a great deal of attention has been focused on Lasso and Dantzig selector ...
17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown paramet...
37 pagesWe consider the problem of estimating a sparse linear regression vector $\beta^*$ under a ga...
We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties ...
Nowadays an increasing amount of data is available and we have to deal with models in high dimension...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
International audienceWe consider the problems of estimation and selection of parameters endowed wit...
We consider the problem of estimating a function f(0) in logistic regression model. We propose to es...
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...
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overd...
We consider the problem of estimating a function $f_{0}$ in logistic regression model. We propose to...
We consider a multivariate finite mixture of Gaussian regression models for high-dimensional data, w...
In this paper, we propose an algorithm encouraging group sparsity under some convex constraint. It s...
We consider the problem of estimating a function f0 in logistic regression model. We propose to esti...
During the last few years, a great deal of attention has been focused on Lasso and Dantzig selector ...
17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown paramet...
37 pagesWe consider the problem of estimating a sparse linear regression vector $\beta^*$ under a ga...
We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties ...
Nowadays an increasing amount of data is available and we have to deal with models in high dimension...
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverl...
International audienceWe consider the problems of estimation and selection of parameters endowed wit...
We consider the problem of estimating a function f(0) in logistic regression model. We propose to es...
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...
This paper studies the sensitivity to the observations of the block/group Lasso solution to an overd...
We consider the problem of estimating a function $f_{0}$ in logistic regression model. We propose to...
We consider a multivariate finite mixture of Gaussian regression models for high-dimensional data, w...
In this paper, we propose an algorithm encouraging group sparsity under some convex constraint. It s...
We consider the problem of estimating a function f0 in logistic regression model. We propose to esti...
During the last few years, a great deal of attention has been focused on Lasso and Dantzig selector ...
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