Improvement of the mixed Liu estimator applying Jackknife method in linear regression models

Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:

JR_JSMTA-2-1_012

تاریخ نمایه سازی: 19 اردیبهشت 1401

Abstract:

In the presence of multicollinearity in the regression models‎, ‎the ordinary least squares estimator loses its performance‎. ‎Some solutions to reduce the effects of multicollinearity have been proposed‎, ‎including the application of biased estimators such as Liu estimate and estimation under linear restrictions‎. ‎But due to the Liu estimator being biased‎, ‎the Jackknife method has been introduced to reduce the bias‎. ‎In this paper‎, ‎we will examine the Jackknifed Liu estimator and propose a new estimator under stochastic linear restrictions namely stochastic restricted Jackknifed Liu estimator‎. ‎A simulation study is conducted to investigate the performance of this new estimator using two measures namely mean squared errors and absolute bias‎. ‎From simulation study results‎, ‎we find that the new estimator outperforms the OLS and Liu estimators‎, ‎and it is superior to both OLS and Liu estimators‎, ‎using the mean squared errors and absolute bias criteria‎.

Authors

Mahtab Taladezfouli

Education Research Institute, Department of Education, Khuzestan, Ahvaz

Abdol-Rahman Rasekh

Department of Statistics, Shahid Chamran University of Ahvaz

Babak Babadi

Department of Statistics, Shahid Chamran University of Ahvaz