Performance of Some Biasing Parameter Estimates for Liu Estimator in Linear Regression Model with Multicollinearity
Keywords:
linear regression, multicollinearity, mean square error, Liu estimator, biasing parameterAbstract
In multiple linear regression models, the multicollinearity occurs when the explanatory variables in a regression model are correlated. The multicollinearity effects on the ordinary least squares estimation method since the estimated regression coefficients become unstable and difficult to interpret in the presence of multicollinearity. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with biasing parameter . The purpose of this research is to investigate the performance of some biasing parameter estimates in Liu regression in the presence of moderate to high correlation among the explanatory variables. The simulation study and application using real data have been performed to evaluate the performance of these biasing parameter estimation methods due to the mean squared errors (MSE). Based on the results from the simulation and application using real data, it can be concluded that the estimators D8, D9, D13 and D15 have a better performance for Liu regression.
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เนื้อหาและข้อมูลที่ปรากฏในบทความที่ตีพิมพ์ในวารสารสถิติประยุกต์และเทคโนโลยีสารสนเทศถือเป็นความคิดเห็นส่วนบุคคลของผู้เขียนแต่ละท่าน ความผิดพลาดของข้อความและผลที่อาจเกิดจากนำข้อความเหล่านั้นไปใช้ผู้เขียนบทความจะเป็นผู้รับผิดชอบแต่เพียงผู้เดียว บทความ ข้อมูล เนื้อหา รูปภาพ ฯลฯ ที่ได้รับการตีพิมพ์ในวารสารถือเป็นลิขสิทธิ์ของวารสาร หากบุคคลหรือหน่วยงานใดต้องการนำทั้งหมดหรือส่วนหนึ่งส่วนใดไปเผยแพร่ต่อหรือเพื่อกระทำการใดๆ จะต้องได้รับอนุญาตเป็นลายลักอักษรณ์จากวารสาร ก่อนเท่านั้น
