More Efficient Estimation Strategy for (k-d) Class Estimator in Existence of Multicollinearity and Heteroscedasticity: Some Monte Carlo Simulation Evidence

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ID: 316163
2023
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Abstract
The typical linear regression model does this to have some sort of heteroscedasticity in the error terms and linear correlation in the regressors. The ordinary least squares estimates are significantly impacted by each of these issues. When these assumptions violated in any multiple linear regression model then ordinary least square estimator happen to unstable and no longer remain best linear unbiased estimator. Therefore, in attempt to tackle the issue of Multicollinearity the rigid, Liu and (k-d) regression exist and easily accessible in literature. The adaptive estimator was recommended to obtain an efficient estimator in comparison to the conventional least square estimator to address the problem of heteroscedasticity. This current work suggests the improved method of adaptation for (kd) class estimator to get more efficient results when dealing with multicollinearity and heteroscedasticity occur at same time. All the numerical work is done by using simulation scheme Monte Carlo, with different degrees of collinearity, severity (existence) of heteroscedasticity, and sample size to assess the performance of the suggested estimator. The simulation results provide best performance of adaptive (k-d) class estimator which is our proposed estimator.
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imported_1780933900_6a26e50c2a11d Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Waqas Makhdoom, Muhammad Aslam
Journal Journal of Statistics
Year 2023
DOI
DOI not found
URL
https://jstat.gcu.edu.pk/index.php/jstat/article/view/17
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