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Performance analysis of the preliminary test estimator with series of stochastic restrictions

Author

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  • M. H. Karbalaee
  • M. Arashi
  • S. M. M. Tabatabaey

Abstract

In this paper, the problem of estimation of the regression coefficients in a multiple regression model is considered under the multicollinearity situation when there are series of stochastic linear restrictions available on the regression parameter vector. We have considered the preliminary test ridge regression estimators (PTRREs) based on the Wald, likelihood ratio, and lagrangian multiplier tests. Tables for the maximum and minimum guaranteed efficiency of the PTRREs are obtained, which allow us to determine the optimum choice of the level of significance corresponding to the optimum estimator. Some numerical results support the findings.

Suggested Citation

  • M. H. Karbalaee & M. Arashi & S. M. M. Tabatabaey, 2018. "Performance analysis of the preliminary test estimator with series of stochastic restrictions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(1), pages 1-17, January.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:1:p:1-17
    DOI: 10.1080/03610926.2017.1300275
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    Cited by:

    1. Waleed B. Altukhaes & Mahdi Roozbeh & Nur A. Mohamed, 2024. "Robust Liu Estimator Used to Combat Some Challenges in Partially Linear Regression Model by Improving LTS Algorithm Using Semidefinite Programming," Mathematics, MDPI, vol. 12(17), pages 1-23, September.

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