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COMPSTAT 2008 pp 359–370Cite as

Fast Robust Variable Selection

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Abstract

We discuss some computationally efficient procedures for robust variable selection in linear regression. A key component in these procedures is the computation of robust correlations between pairs of variables. We show that the robust variable selection procedures can easily handle missing data under the assumption that data are missing completely at random.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 S9, B-9000, Gent, Belgium

    Stefan Aelst

  2. Department of Statistics, University of Dhaka, Dhaka-1000, Bangladesh

    Jafar A. Khan

  3. Department of Statistics, University of British Columbia, Vancouver, British Columbia, V6T-1Z2, Canada

    Ruben H. Zamar

Authors
  1. Stefan Aelst
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  2. Jafar A. Khan
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  3. Ruben H. Zamar
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Corresponding author

Correspondence to Stefan Aelst .

Editor information

Editors and Affiliations

  1. Faculdade de Economia, Rua Dr. Roberto Frias, 4200-464, Porto, Portugal

    Paula Brito

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© 2008 Physica-Verlag Heidelberg

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Aelst, S., Khan, J.A., Zamar, R.H. (2008). Fast Robust Variable Selection. In: Brito, P. (eds) COMPSTAT 2008. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2084-3_30

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