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


Ozkale M. R. , Arıcan E.

COMPUTATIONAL STATISTICS, cilt.34, ss.683-712, 2019 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 34 Konu: 2
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s00180-018-0851-6
  • Dergi Adı: COMPUTATIONAL STATISTICS
  • Sayfa Sayısı: ss.683-712

Özet

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.