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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.
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Preprints, Working Papers, ...
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https://hal.archives-ouvertes.fr/hal-01188376
Contributor : Marie-Luce Taupin Connect in order to contact the contributor
Submitted on : Saturday, August 29, 2015 - 4:19:49 PM
Last modification on : Tuesday, September 7, 2021 - 3:32:56 PM
Long-term archiving on: : Monday, November 30, 2015 - 10:13:16 AM

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

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

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