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Performance of the restricted almost unbiased type principal components estimators in linear regression model

Author

Listed:
  • Yalian Li

    (Chongqing University)

  • Hu Yang

    (Chongqing University)

Abstract

In this paper, two new classes of estimators called the restricted almost unbiased ridge-type principal components estimator and the restricted almost unbiased Liu-type principal components estimator are introduced. For the two cases when the restrictions are true and not true, necessary and sufficient conditions for the superiority of the proposed estimators are derived and compared, respectively. Finally, A Monte Carlo simulation study is given to illustrate the performance of the proposed estimators.

Suggested Citation

  • Yalian Li & Hu Yang, 2019. "Performance of the restricted almost unbiased type principal components estimators in linear regression model," Statistical Papers, Springer, vol. 60(1), pages 19-34, February.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0821-4
    DOI: 10.1007/s00362-016-0821-4
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    References listed on IDEAS

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    1. Groß, Jürgen, 2003. "Restricted ridge estimation," Statistics & Probability Letters, Elsevier, vol. 65(1), pages 57-64, October.
    2. M. Hubert & P. Wijekoon, 2006. "Improvement of the Liu estimator in linear regression model," Statistical Papers, Springer, vol. 47(3), pages 471-479, June.
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    Cited by:

    1. Maolin Cheng & Bin Liu, 2019. "Analysis on the Influence of China’s Energy Consumption on Economic Growth," Sustainability, MDPI, vol. 11(14), pages 1-25, July.

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