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Combining two-parameter and principal component regression estimators

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Abstract

This paper is concerned with the parameter estimation in linear regression model. To overcome the multicollinearity problem, a new class of estimator, namely principal component two-parameter (PCTP) estimator is proposed. The superiority of the new estimator over the principal component regression (PCR) estimator, the r − k class estimator, the r − d class estimator and the two-parameter estimator proposed by Yang and Chang (Commun Stat Theory Methods 39:923–934 2010) are discussed with respect to the mean squared error matrix (MSEM) criterion. Furthermore, we give a numerical example and a simulation study to illustrate some of the theoretical results.

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, Chongqing University, Chongqing, 400030, People’s Republic of China

    Xinfeng Chang & Hu Yang

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  1. Xinfeng Chang
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  2. Hu Yang
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Correspondence to Xinfeng Chang.

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Chang, X., Yang, H. Combining two-parameter and principal component regression estimators. Stat Papers 53, 549–562 (2012). https://doi.org/10.1007/s00362-011-0364-7

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  • DOI: https://doi.org/10.1007/s00362-011-0364-7

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