Jackknifing K-L estimator in generalized linear models
عنوان مقاله: Jackknifing K-L estimator in generalized linear models
شناسه ملی مقاله: JR_IJNAA-12-0_160
منتشر شده در در سال 1400
شناسه ملی مقاله: JR_IJNAA-12-0_160
منتشر شده در در سال 1400
مشخصات نویسندگان مقاله:
- - - Department of Economics, College of Administration and Economics, University of Anbar, Anbar, Iraq
- - - Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
خلاصه مقاله:
- - - Department of Economics, College of Administration and Economics, University of Anbar, Anbar, Iraq
- - - Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
It is a challenge in the real application when modelling the relationship between the response variable and several explanatory variables when the existence of collinearity. Traditionally, in order to avoid this issue, several shrinkage estimators are proposed. Among them is the Kibria and Lukman estimator (K-L). In this study, a jackknifed version of the K-L estimator is proposed in the generalized linear model that combines the Jackknife procedure with the K-L estimator to reduce the biasedness. Our Monte Carlo simulation results and the real data application related to the inverse Gaussian regression model suggest that the proposed estimator can bring significant improvement relative to other competitor estimators, in terms of absolute bias and mean squared error.
کلمات کلیدی: Collinearity, K-L estimator, Inverse Gaussian regression model, Jackknife estimator, Monte Carlo simulation
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1561540/