Optimal stochastic restricted logistic estimator

Nagarajah Varathan () and Pushpakanthie Wijekoon
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Nagarajah Varathan: University of Peradeniya
Pushpakanthie Wijekoon: University of Peradeniya

Statistical Papers, 2021, vol. 62, issue 2, No 18, 985-1002

Abstract: Abstract It is well known that the use of prior information in the logistic regression improves the estimates of regression coefficients when multicollinearity presents. This prior information may be in the form of exact or stochastic linear restrictions. In this article, in the presence of stochastic linear restrictions, we propose a new efficient estimator, named Stochastic restricted optimal logistic estimator for the parameters in the logistic regression models when the multicollinearity presents. Further, conditions for the superiority of the new optimal estimator over some existing estimators are derived with respect to the mean square error matrix sense. Moreover, a Monte Carlo simulation study and a real data example are provided to illustrate the performance of the proposed optimal estimator in the scalar mean square error sense.

Keywords: Logistic regression; Multicollinearity; Optimal estimator; Mean square error; Scalar mean square error (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00362-019-01121-y

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