Non-asymptotic oracle inequalities for the Lasso and Group Lasso in high dimensional logistic model
ESAIM: Probability and Statistics, Tome 20 (2016), pp. 309-331.

We consider the problem of estimating a function f 0 in logistic regression model. We propose to estimate this function f 0 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].

Reçu le :
Accepté le :
DOI : 10.1051/ps/2015020
Classification : 62H12, 62J12, 62J07, 62G20
Mots-clés : Logistic model, Lasso, Group Lasso, high-dimensional, oracle inequality
Kwemou, Marius 1, 2

1 Laboratoire de Mathématique et modélisation d’Evry UMR CNRS 8071- USC INRA, Université d’Évry Val d’Essonne, Evry, France.
2 LERSTAD, Université Gaston Berger de Saint-Louis, Sénégal.
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     title = {Non-asymptotic oracle inequalities for the {Lasso} and {Group} {Lasso} in high dimensional logistic model},
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Kwemou, Marius. Non-asymptotic oracle inequalities for the Lasso and Group Lasso in high dimensional logistic model. ESAIM: Probability and Statistics, Tome 20 (2016), pp. 309-331. doi : 10.1051/ps/2015020. http://www.numdam.org/articles/10.1051/ps/2015020/

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