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Combining the Liu-type estimator and the principal component regression estimator

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  • Deniz Inan

Abstract

In this study a new two-parameter estimator which includes the ordinary least squares, the principal components regression (PCR) and the Liu-type estimator is proposed. Conditions for the superiority of this new estimator over the PCR, r–k class estimator and Liu-type estimator are derived. Furthermore the performance of this estimator is compared with the other estimators in different conditions with simulation studies. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Deniz Inan, 2015. "Combining the Liu-type estimator and the principal component regression estimator," Statistical Papers, Springer, vol. 56(1), pages 147-156, February.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:1:p:147-156
    DOI: 10.1007/s00362-013-0571-5
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    References listed on IDEAS

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    1. 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.
    2. 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. Heewon Park & Sadanori Konishi, 2020. "Sparse common component analysis for multiple high-dimensional datasets via noncentered principal component analysis," Statistical Papers, Springer, vol. 61(6), pages 2283-2311, December.

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