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