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Prediction Divergence Criterion for Model Selection in the Logistic Regression

ContributorsMiglioli, Cesare
Master program titleMaster of Science in Statistics
Defense date2018
Abstract

In this Master Thesis, we have analytically derived and numerically implemented three estimators of the Prediction Divergence Criterion (Avella-Medina et al., working paper) for Model Selection within the logistic regression framework. After the validation of these estimators by means of simulations, we have performed Model Selection both when the order of the variables was known in advance and when the order was correct but decided by an already existing algorithm, namely the binary lasso (Friedman et al., 2010). Finally we have produced evidences of the good performance of two of these estimators, one derived from the L2 norm error measure and the other from the binomial deviance, respectively in highly and moderately correlated settings. They have been proven better, to the extension of the simulation study, than the defaults methods, based on 10-fold Cross Validation, currently available in the glmnet(Friedman et al., 2017) R package.

Keywords
  • Model selection
  • Logistic regression
  • Divergence
  • Big data
  • High dimensions
  • Binary lasso
  • Cross-validation
  • Stopping criteria
Citation (ISO format)
MIGLIOLI, Cesare. Prediction Divergence Criterion for Model Selection in the Logistic Regression. Master, 2018.
Main files (1)
Master thesis
Identifiers
  • PID : unige:102928
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291downloads

Technical informations

Creation25/02/2018 19:01:00
First validation25/02/2018 19:01:00
Update time15/03/2023 07:58:26
Status update15/03/2023 07:58:25
Last indexation31/10/2024 09:49:08
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