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.
Document type :
Preprints, Working Papers, ...
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01188376
Contributor : Marie-Luce Taupin <>
Submitted on : Saturday, August 29, 2015 - 4:19:49 PM
Last modification on : Friday, July 20, 2018 - 11:13:29 AM
Long-term archiving on : Monday, November 30, 2015 - 10:13:16 AM

Files

logit_histo_soumis.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01188376, version 1
  • ARXIV : 1508.07537

Citation

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

Share

Metrics

Record views

490

Files downloads

128