Model selection in logistic regression
Statistics Theory
2015-09-01 v1 Statistics Theory
Abstract
This paper is devoted to model selection in logistic regression. We extend the model selection principle introduced by Birg\'e 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.
Cite
@article{arxiv.1508.07537,
title = {Model selection in logistic regression},
author = {Marius Kwemou and Marie-Luce Taupin and Anne-Sophie Tocquet},
journal= {arXiv preprint arXiv:1508.07537},
year = {2015}
}