MODEL SELECTION IN LOGISTIC REGRESSION - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year :

MODEL SELECTION IN LOGISTIC REGRESSION

Abstract

This paper is devoted to model selection in logistic regression. We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. This selection is done by using penalized maximum likelihood criteria. We propose in this context a completely data-driven criteria based on the slope heuristics. We prove non asymptotic oracle inequalities for selected estimators. Theoretical results are illustrated through simulation studies.
Fichier principal
Vignette du fichier
logit_histo_soumis.pdf (249.94 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01188376 , version 1 (29-08-2015)

Identifiers

Cite

Marius Kwemou, Marie-Luce Taupin, Anne-Sophie Tocquet. MODEL SELECTION IN LOGISTIC REGRESSION. 2015. ⟨hal-01188376⟩
272 View
485 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More