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Outlier Detection and Robust Variable Selection for Least Angle Regression

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Computational Science and Its Applications – ICCSA 2014 (ICCSA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8581))

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Abstract

The problem of selecting a parsimonious subset of variables from a large number of predictors in a regression model is a topic of high importance. When the data contains vertical outliers and/or leverage points, outlier detection and variable selection are inseparable problems. Therefore a robust method that can simultaneously detect outliers and select variables is needed. An outlier detection and robust variable selection method is introduced that combines robust least angle regression with least trimmed squares regression on jack-knife subsets. In a second stage the detected outliers are removed and standard least angle regression is applied on the cleaned data to robustly sequence the predictor variables in order of importance. The performance of this method is evaluated by simulations that contain vertical outliers and high leverage points. The results of the simulation study show the good performance of this method in both outlier detection and robust variable selection.

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Author information

Authors and Affiliations

  1. DMA-Department of Mathematics and Applications, CMAT-Centre of Mathematics, University of Minho, Guimarães, Portugal

    Shirin Shahriari, Susana Faria & A. Manuela Gonçalves

  2. Department of Mathematics, K.U. Leuven, Leuven, Belgium

    Stefan Van Aelst

  3. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Stefan Van Aelst

Authors
  1. Shirin Shahriari
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  2. Susana Faria
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  3. A. Manuela Gonçalves
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  4. Stefan Van Aelst
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Editor information

Editors and Affiliations

  1. School of Engineering, University of Basilicata, 85100, Potenza, Italy

    Beniamino Murgante

  2. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

  3. Department of Production and Systems, University of Minho, 4710-057, Braga, Portugal

    Ana Maria A. C. Rocha

  4. DICAR, Polytecnico di Bari, 70125, Bari, Italy

    Carmelo Torre

  5. University of Minho, Braga, Portugal

    Jorge Gustavo Rocha  & Maria Irene Falcão  & 

  6. Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics,, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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© 2014 Springer International Publishing Switzerland

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Shahriari, S., Faria, S., Gonçalves, A.M., Van Aelst, S. (2014). Outlier Detection and Robust Variable Selection for Least Angle Regression. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8581. Springer, Cham. https://doi.org/10.1007/978-3-319-09150-1_37

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  • DOI: https://doi.org/10.1007/978-3-319-09150-1_37

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09149-5

  • Online ISBN: 978-3-319-09150-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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