Jackknifing K-L estimator in generalized linear models | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 2093-2104 اصل مقاله (511.5 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.6039 | ||
| نویسندگان | ||
| Abed Ali Hamad1؛ Zakariya Yahya Algamal* 2 | ||
| 1Department of Economics, College of Administration and Economics, University of Anbar, Anbar, Iraq | ||
| 2Department of Statistics and Informatics, University of Mosul, Mosul, Iraq | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Collinearity؛ K-L estimator؛ Inverse Gaussian regression model؛ Jackknife estimator؛ Monte Carlo simulation | ||
| مراجع | ||
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