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

Document Type : Original Scientific Paper

Authors

1 Education Research Institute, Department of Education, Khuzestan, Ahvaz, Iran

2 Department of Statistics, Shahid Chamran University of Ahvaz, Ahvaz, Iran

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.

Keywords

References

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Journal of Statistical Modelling: Theory and Applications
Volume 2, Issue 1
January 2021
Pages 151-166

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History

  • Receive Date: 14 September 2020
  • Revise Date: 30 August 2021
  • Accept Date: 12 September 2021

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