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Robust sliced inverse regression procedures

Author

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  • Gather, Ursula
  • Hilker, Torsten
  • Becker, Claudia

Abstract

Sliced Inverse Regression (SIR) is a promising technique for the purpose of dimension reduction. Several properties of this relatively new method have been examined already, but little attention has been paid to robustness aspects. We show that SIR is very sensitive towards outliers in the data. Therefore a generalized estimation procedure which allows for robustness properties, especially for a high breakdown point, is proposed.

Suggested Citation

  • Gather, Ursula & Hilker, Torsten & Becker, Claudia, 1998. "Robust sliced inverse regression procedures," Technical Reports 1998,22, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199822
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    File URL: https://www.econstor.eu/bitstream/10419/77188/2/1998-22.pdf
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    Cited by:

    1. Zhou, Jingke & Xu, Wangli & Zhu, Lixing, 2015. "Robust estimating equation-based sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 99-118.
    2. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

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