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Predictive performance of linear regression models

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  • M. Özkale

Abstract

In this paper, the cross-validation methods namely the $$C_{p}$$ C p , PRESS and GCV are presented under the multiple linear regression model when multicollinearity exists and additional information imposes restrictions among the parameters that should hold in exact terms. The selection of the biasing parameters are given so as to minimize the cross-validation methods. An example is given which illustrates the comprehensive predictive assessment of various estimators and shows the usefullness of computing. Besides, the performance of the estimators under several different conditions is examined via a simulation study. The results displayed that the biased estimator versions and the restricted form of the biased estimator versions of cross-validation methods give better predictive performance in the presence of multicollinearity. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • M. Özkale, 2015. "Predictive performance of linear regression models," Statistical Papers, Springer, vol. 56(2), pages 531-567, May.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:2:p:531-567
    DOI: 10.1007/s00362-014-0596-4
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    References listed on IDEAS

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    1. 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.
    2. Liu, Honghu & Weiss, Robert E. & Jennrich, Robert I. & Wenger, Neil S., 1999. "PRESS model selection in repeated measures data," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 169-184, April.
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