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

Authors :
Özkale, M. Revan
Arıcan, Engin
Source :
Computational Statistics. Jun2019, Vol. 34 Issue 2, p683-712. 30p.
Publication Year :
2019

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09434062
Volume :
34
Issue :
2
Database :
Academic Search Index
Journal :
Computational Statistics
Publication Type :
Academic Journal
Accession number :
136274183
Full Text :
https://doi.org/10.1007/s00180-018-0851-6