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Robust stepwise regression

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  • C. Agostinelli

Abstract

The selection of an appropriate subset of explanatory variables to use in a linear regression model is an important aspect of a statistical analysis. Classical stepwise regression is often used with this aim but it could be invalidated by a few outlying observations. In this paper, we introduce a robust F-test and a robust stepwise regression procedure based on weighted likelihood in order to achieve robustness against the presence of outliers. The introduced methodology is asymptotically equivalent to the classical one when no contamination is present. Some examples and simulation are presented.

Suggested Citation

  • C. Agostinelli, 2002. "Robust stepwise regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(6), pages 825-840.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:6:p:825-840
    DOI: 10.1080/02664760220136168
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    1. Agostinelli, Claudio, 2002. "Robust model selection in regression via weighted likelihood methodology," Statistics & Probability Letters, Elsevier, vol. 56(3), pages 289-300, February.
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    Cited by:

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    2. Zamri Ahmad & Haslindar Ibrahim & Jasman Tuyon, 2017. "Behavior of fund managers in Malaysian investment management industry," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 9(3), pages 205-239, August.
    3. Suman Majumder & Adhidev Biswas & Tania Roy & Subir Kumar Bhandari & Ayanendranath Basu, 2021. "Statistical inference based on a new weighted likelihood approach," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(1), pages 97-120, January.
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    7. Beste Hamiye Beyaztas & Soutir Bandyopadhyay & Abhijit Mandal, 2021. "A robust specification test in linear panel data models," Papers 2104.07723, arXiv.org.

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