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An improved and efficient biased estimation technique in logistic regression model

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  • Yasin Asar
  • Jibo Wu

Abstract

In this article, we propose a new improved and efficient biased estimation method which is a modified restricted Liu-type estimator satisfying some sub-space linear restrictions in the binary logistic regression model. We study the properties of the new estimator under the mean squared error matrix criterion and our results show that under certain conditions the new estimator is superior to some other estimators. Moreover, a Monte Carlo simulation study is conducted to show the performance of the new estimator in the simulated mean squared error and predictive median squared errors sense. Finally, a real application is considered.

Suggested Citation

  • Yasin Asar & Jibo Wu, 2020. "An improved and efficient biased estimation technique in logistic regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(9), pages 2237-2252, May.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:9:p:2237-2252
    DOI: 10.1080/03610926.2019.1568494
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