On Restricted Shrinkage Jackknife Biased Estimator for Restricted Linear Regression Model | ||
| Journal of University of Anbar for Pure Science | ||
| Article 33, Volume 17, Issue 2, December 2023, Pages 245-253 PDF (598.53 K) | ||
| Document Type: Research Paper | ||
| DOI: 10.37652/juaps.2023.181574 | ||
| Authors | ||
| Ahmed A. Mohammed* 1; Feras Shaker Algareri2 | ||
| 1Department of Mathematics, College of Education for Pure Science, University of Anbar, Anbar, Iraq; | ||
| 2Anbar University, College of Education for Pure Sciences, Department of Mathematics. | ||
| Abstract | ||
| In restricted linear regression model, more methods proposed to address the Multicollinearity problem and the high variance. For example, shrinkage biased estimation and optimization (Lagrange function). In this paper, we propose new biased estimator based on philosophy of Jackknife with the restricted least squares estimator. A new estimator called Restricted Shrinkage Jackknife estimator (RSJ). Also, we show that the statistical properties of new estimator with some theorems to compare the performance of new estimator with some restricted estimators and we make simulation study of these estimators. Finally, a real data has been taken into consideration to demonstrate how well the estimators perform. | ||
| Keywords | ||
| Restricted regression model; jackknifed biased estimator; Multicollinearity problem; Two parameters estimator | ||
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| References | ||
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