We consider the problem of estimating a function f0 in logistic regression model. We propose to estimate this function f0 by a sparse approximation build as a linear combination of elements of a given dictionary of p functions. This sparse approximation is selected by the Lasso or Group Lasso procedure. In this context, we state non asymptotic oracle inequalities for Lasso and Group Lasso under restricted eigenvalue assumption as introduced in [P.J. Bickel, Y. Ritov and A.B. Tsybakov, Ann. Statist. 37 (2009) 1705–1732]
Abstract. We consider a high-dimensional regression model with a possible change-point due to a cova...
Abstract: This paper studies oracle properties of!1-penalized least squares in nonparametric regress...
In a general counting process setting, we consider the problem of obtaining a prognostic on the surv...
We consider the problem of estimating a function f(0) in logistic regression model. We propose to es...
We consider the problem of estimating a function $f_{0}$ in logistic regression model. We propose to...
We consider the problem of estimating a function f0 in logistic regression model. We propose to esti...
We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties ...
17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown paramet...
We define the group lasso estimator for the natural parameters of the exponential families of distri...
Nowadays an increasing amount of data is available and we have to deal with models in high dimension...
37 pagesWe consider the problem of estimating a sparse linear regression vector $\beta^*$ under a ga...
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation ac...
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation a...
Abstract. This paper establishes non-asymptotic oracle inequalities for the prediction error and est...
Revise and add further explanationsMixture of experts (MoE) has a well-principled finite mixture mod...
Abstract. We consider a high-dimensional regression model with a possible change-point due to a cova...
Abstract: This paper studies oracle properties of!1-penalized least squares in nonparametric regress...
In a general counting process setting, we consider the problem of obtaining a prognostic on the surv...
We consider the problem of estimating a function f(0) in logistic regression model. We propose to es...
We consider the problem of estimating a function $f_{0}$ in logistic regression model. We propose to...
We consider the problem of estimating a function f0 in logistic regression model. We propose to esti...
We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties ...
17 pagesWe consider the linear regression model with Gaussian error. We estimate the unknown paramet...
We define the group lasso estimator for the natural parameters of the exponential families of distri...
Nowadays an increasing amount of data is available and we have to deal with models in high dimension...
37 pagesWe consider the problem of estimating a sparse linear regression vector $\beta^*$ under a ga...
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation ac...
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation a...
Abstract. This paper establishes non-asymptotic oracle inequalities for the prediction error and est...
Revise and add further explanationsMixture of experts (MoE) has a well-principled finite mixture mod...
Abstract. We consider a high-dimensional regression model with a possible change-point due to a cova...
Abstract: This paper studies oracle properties of!1-penalized least squares in nonparametric regress...
In a general counting process setting, we consider the problem of obtaining a prognostic on the surv...