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Activity Number: 240 - Computationally Intensive Methods for Estimation and Inference
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #323619
Title: Maximum Likelihood Versus Alternative Regularized Estimators for Logistic Regression Models
Author(s): Gabriel Ruiz* and Subir Ghosh
Companies: Department of Statistics, University of California, Riverside and Department of Statistics, University of California, Riverside
Keywords: Alternative Regularized Estimator ; Logistic Regression ; Maximum Likelihood ; Tuning Parameter
Abstract:

The alternative regularized estimators (AREs) are proposed for estimating the parameters of logistic regression models and compared with the maximum likelihood estimators (MLEs). The AREs are dependent on a tuning parameter and the proposed alternative estimators (AEs) which are not regularized. The values of the tuning parameters are obtained to make AREs to be approximately equal to MLEs using a proposed method and the process is explained with a real data as well as a simulated study.


Authors who are presenting talks have a * after their name.

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