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Performance of some new Liu parameters for the linear regression model

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  • Muhammad Qasim
  • Muhammad Amin
  • Talha Omer

Abstract

This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters.

Suggested Citation

  • Muhammad Qasim & Muhammad Amin & Talha Omer, 2020. "Performance of some new Liu parameters for the linear regression model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(17), pages 4178-4196, September.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:17:p:4178-4196
    DOI: 10.1080/03610926.2019.1595654
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    Cited by:

    1. Iqra Babar & Hamdi Ayed & Sohail Chand & Muhammad Suhail & Yousaf Ali Khan & Riadh Marzouki, 2021. "Modified Liu estimators in the linear regression model: An application to Tobacco data," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-13, November.
    2. Adewale F. Lukman & B. M. Golam Kibria & Cosmas K. Nziku & Muhammad Amin & Emmanuel T. Adewuyi & Rasha Farghali, 2023. "K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model," Mathematics, MDPI, vol. 11(2), pages 1-14, January.

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