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June 2022 A general restricted estimator in binary logistic regression in the presence of multicollinearity
Gargi Tyagi, Shalini Chandra
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Braz. J. Probab. Stat. 36(2): 287-314 (June 2022). DOI: 10.1214/21-BJPS527


The presence of multicollinearity adversely affects the inferential properties of the maximum likelihood (ML) estimator in logistic regression model. It is a well-established fact that the use of restrictions lowers the effect of multicollinearity. In this article, an alternative to the ML estimator has been introduced by combining the exact prior information into the logistic rk class (Lrk) estimator. The estimator is named a logistic restricted rk class estimator. Another estimator, logistic restricted PCR estimator, is also developed as a special case of the LRrk estimator. The asymptotic mean squared error (MSE) matrix properties of the estimators are studied and necessary and sufficient conditions are derived. Further, a Monte Carlo simulation study is performed to compare the performance of the estimators in terms of the scalar MSE and the prediction MSE. It is found that the proposed estimators perform better than the existing estimators in most of the cases considered. Moreover, a numerical example has also been presented for comparing the performance of the estimators.


The authors are grateful to the editor and referees for their valuable comments and suggestions, which certainly improved the quality and presentation of the paper.


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Gargi Tyagi. Shalini Chandra. "A general restricted estimator in binary logistic regression in the presence of multicollinearity." Braz. J. Probab. Stat. 36 (2) 287 - 314, June 2022.


Received: 1 March 2021; Accepted: 1 November 2021; Published: June 2022
First available in Project Euclid: 10 May 2022

Digital Object Identifier: 10.1214/21-BJPS527

Keywords: logistic regression , mean squared error , multicollinearity , predicted mean squared error , restrictions

Rights: Copyright © 2022 Brazilian Statistical Association


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Vol.36 • No. 2 • June 2022
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