A first-order approximated jackknifed ridge estimator in binary logistic regression
| Autor: | Engin Arıcan, M. Revan Özkale |
|---|---|
| Přispěvatelé: | Çukurova Üniversitesi |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Mean squared error Ridge logistic estimator Logistic regression 01 natural sciences Iteratively reweighted least squares 010104 statistics & probability 0502 economics and business Statistics Statistics::Methodology Receiving operating characteristic 0101 mathematics 050205 econometrics Mathematics 05 social sciences Confidence interval Estimator Covariance Ridge (differential geometry) Computational Mathematics Multicollinearity Statistics Probability and Uncertainty Jackknife resampling Principal components logistic regression estimator |
| Popis: | 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. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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