A new kind of stochastic restricted biased estimator for logistic regression model

• Published in: Journal of applied statistics Vol. 48; no. 9; pp. 1559 - 1578
• Main Authors: Alheety, M. I., Månsson, Kristofer, Golam Kibria, B. M.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 2021; Taylor & Francis Ltd
• Subjects: Logistic regression; maximum likelihood estimator; Maximum likelihood estimators; mean squared error matrix; Primary 62J05; Regression models; ridge regression; Secondary 62J07; simulation study; Statistical analysis; Statistical methods; stochastic restricted estimator; Logistic regression; maximum likelihood estimator; Secondary 62J07; stochastic restricted estimator; Primary 62J05; ridge regression; simulation study; mean squared error matrix
• ISSN: 0266-4763; 1360-0532; 1360-0532
• DOI: 10.1080/02664763.2020.1769576

In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator.