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Robust model selection criteria for robust Liu estimator

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  • Çetin, Meral

Abstract

In linear regression analysis, outliers often have large influence in the model/variable selection process. The aim of this study is to select the subsets of independent variables which explain dependent variables in the presence of multicollinearity, outliers and possible departures from the normality assumption of the error distribution in robust regression analysis. In this study to overcome this combined problem of multicollinearity and outliers, we suggest to use robust selection criterion with Liu and Liu-type M(LM) estimators.

Suggested Citation

  • Çetin, Meral, 2009. "Robust model selection criteria for robust Liu estimator," European Journal of Operational Research, Elsevier, vol. 199(1), pages 21-24, November.
    • Handle: RePEc:eee:ejores:v:199:y:2009:i:1:p:21-24
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    References listed on IDEAS

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    1. Hurvich, Clifford M. & Tsai, Chih-Ling, 1990. "Model selection for least absolute deviations regression in small samples," Statistics & Probability Letters, Elsevier, vol. 9(3), pages 259-265, March.
    2. Ronchetti, Elvezio, 1985. "Robust model selection in regression," Statistics & Probability Letters, Elsevier, vol. 3(1), pages 21-23, February.
    3. Olcay Arslan & Nedret Billor, 2000. "Robust Liu estimator for regression based on an M-estimator," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(1), pages 39-47.
    4. Suzanne Sommer & Richard M. Huggins, 1996. "Variables Selection Using the Wald Test and a Robust CP," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(1), pages 15-29, March.
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    Cited by:

    1. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
    2. Beynon, Malcolm J. & Andrews, Rhys & Boyne, George A., 2010. "Evidence-based modelling of strategic fit: An introduction to RCaRBS," European Journal of Operational Research, Elsevier, vol. 207(2), pages 886-896, December.
    3. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.

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