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Superiority of the r-d class estimator over some estimators by the mean square error matrix criterion

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  • Özkale, M. Revan
  • KaçIranlar, Selahattin

Abstract

KaçIranlar, and SakallIoglu, [2001. Combining the Liu estimator and the principal component regression estimator. Comm. Statist. Theory Methods 30, 2699-2705] introduced the r-d class estimator which is a general estimator of the ordinary least squares (OLS), the principal components regression (PCR) and the Liu estimators. In this paper, we derive conditions for the superiority of the r-d class estimator over each of these estimators and the r-k class estimator by the matrix mean square error (MMSE) criterion. Also, we suggest tests to verify if these conditions are indeed satisfied.

Suggested Citation

  • Özkale, M. Revan & KaçIranlar, Selahattin, 2007. "Superiority of the r-d class estimator over some estimators by the mean square error matrix criterion," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 438-446, February.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:4:p:438-446
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    References listed on IDEAS

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    1. Sarkar, Nityananda, 1996. "Mean square error matrix comparison of some estimators in linear regressions with multicollinearity," Statistics & Probability Letters, Elsevier, vol. 30(2), pages 133-138, October.
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    Cited by:

    1. Deniz Inan, 2015. "Combining the Liu-type estimator and the principal component regression estimator," Statistical Papers, Springer, vol. 56(1), pages 147-156, February.
    2. Xinfeng Chang & Hu Yang, 2012. "Combining two-parameter and principal component regression estimators," Statistical Papers, Springer, vol. 53(3), pages 549-562, August.

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