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A new Jackknifing ridge estimator for logistic regression model | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 176، دوره 13، شماره 1، خرداد 2022، صفحه 2127-2135 اصل مقاله (298.99 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5908 | ||
| نویسندگان | ||
| Nawal Mahmood Hammood* 1؛ Zakariya Yahya Algamal2 | ||
| 1Department of management Information systems, College of Administration and Economics, University of Mosul, Mosul, Iraq. | ||
| 2Department of Statistics and Informatics, University of Mosul, Mosul, Iraq. | ||
| تاریخ دریافت: 15 اردیبهشت 1400، تاریخ پذیرش: 22 مهر 1400 | ||
| چکیده | ||
| In reducing the effects of collinearity, the ridge estimator (RE) has been consistently demonstrated to be an attractive shrinkage method. In application, when the response variable is binary data, the logistic regression model (LRM) is a well-known model. However, it is known that collinearity negatively affects the variance of maximum likelihood estimator of the LRM. To address this problem, a logistic ridge estimator was proposed by several authors. In this work, a Jackknifing logistic ridge estimator (NJLRE) is proposed and derived. The Monte Carlo simulation results recommend that the NJLRE estimator can bring significant improvement relative to other existing estimators. Furthermore, the real application results demonstrate that the NJLRE estimator outperforms both LRE and MLE in terms of predictive performance. | ||
| کلیدواژهها | ||
| Collinearity؛ Jackknife estimator؛ ridge estimator؛ logistic regression model؛ Monte Carlo simulation | ||
| مراجع | ||
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