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A Comparison of Pretest, Stein-Type and Penalty Estimators in Logistic Regression Model

  • Orawan ReangsephetEmail author
  • Supranee Lisawadi
  • Syed Ejaz Ahmed
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

Various estimators are proposed based on the preliminary test and Stein-type strategies to estimate the parameters in a logistic regression model when it is priori suspected that some parameters may be restricted to a subspace. Two different penalty estimators as LASSO and ridge regression are also considered. A Monte Carlo simulation experiment was conducted for different combinations, and the performance of each estimator was evaluated in terms of simulated relative efficiency. The positive-part Stein-type shrinkage estimator is recommended for use since its performance is robust regardless of the reliability of the subspace information. The proposed estimators are applied to a real dataset to appraise their performance.

Keywords

Monte Carlo simulation Logistic regression model Likelihood ratio test Preliminary test estimator Shrinkage estimator Penalty estimator 

Notes

Acknowledgements

We would like to thank all the referees, the Editor, and the Associate Editor for their valuable suggestions on the revision of the article.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Orawan Reangsephet
    • 1
    Email author
  • Supranee Lisawadi
    • 1
  • Syed Ejaz Ahmed
    • 2
  1. 1.Department of Mathematics and StatisticsThammasat UniversityBangkokThailand
  2. 2.Faculty of Mathematics and ScienceBrock UniversitySt. CatharinesCanada

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