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General linear estimators under the prediction error sum of squares criterion in a linear regression model

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  • Xu-Qing Liu
  • Bo Li

Abstract

In this paper, the notion of the general linear estimator and its modified version are introduced using the singular value decomposition theorem in the linear regression model y = X β + e to improve some classical linear estimators. The optimal selections of the biasing parameters involved are theoretically given under the prediction error sum of squares criterion. A numerical example and a simulation study are finally conducted to illustrate the superiority of the proposed estimators.

Suggested Citation

  • Xu-Qing Liu & Bo Li, 2012. "General linear estimators under the prediction error sum of squares criterion in a linear regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1353-1361, December.
    • Handle: RePEc:taf:japsta:v:39:y:2012:i:6:p:1353-1361
    DOI: 10.1080/02664763.2011.646963
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    Cited by:

    1. M. Özkale, 2015. "Predictive performance of linear regression models," Statistical Papers, Springer, vol. 56(2), pages 531-567, May.

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