A first-order approximated jackknifed ridge estimator in binary logistic regression

M. Revan Özkale () and Engin Arıcan
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M. Revan Özkale: Cukurova University
Engin Arıcan: Cukurova University

Computational Statistics, 2019, vol. 34, issue 2, No 13, 683-712

Abstract: Abstract The purpose of this paper is to solve the problem of multicollinearity that affects the estimation of logistic regression model by introducing first-order approximated jackknifed ridge logistic estimator which is more efficient than the first-order approximated maximum likelihood estimator and has smaller variance than the first-order approximated jackknife ridge logistic estimator. Comparisons of the first-order approximated jackknifed ridge logistic estimator to the first-order approximated maximum likelihood, first-order approximated ridge, first-order approximated r-k class and principal components logistic regression estimators according to the bias, covariance and mean square error criteria are done. Three different estimators for the ridge parameter are also proposed. A real data set is used to see the performance of the first-order approximated jackknifed ridge logistic estimator over the first-order approximated maximum likelihood, first-order approximated ridge logistic, first-order approximated r-k class and first-order approximated principal components logistic regression estimators. Finally, two simulation studies are conducted in order to show the performance of the first-order approximated jackknife ridge logistic estimator.

Keywords: Iteratively reweighted least squares; Ridge logistic estimator; Principal components logistic regression estimator; Multicollinearity; Receiving operating characteristic; Confidence interval (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00180-018-0851-6

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