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Robust Regression by Means of S-Estimators

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Robust and Nonlinear Time Series Analysis

Part of the book series: Lecture Notes in Statistics ((LNS,volume 26))

Abstract

There are at least two reasons why robust regression techniques are useful tools in robust time series analysis. First of all, one often wants to estimate autoregressive parameters in a robust way, and secondly, one sometimes has to fit a linear or nonlinear trend to a time series. In this paper we shall develop a class of methods for robust regression, and briefly comment on their use in time series. These new estimators are introduced because of their invulnerability to large fractions of contaminated data. We propose to call them “S-estimators” because they are based on estimators of scale.

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Author information

Authors and Affiliations

  1. CSOO (M 205), Vrije Universiteit Brussel, Pleinlaan 2, B-1050, Brussels, Belgium

    P. Rousseeuw & V. Yohai

  2. Departamento de Matemáticas, Facultad de Ciencias Exactas y Naturales, Ciudad Universitaria, Pabellon 1, 1428, Buenos Aires, Argentina

    P. Rousseeuw & V. Yohai

Authors
  1. P. Rousseeuw
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  2. V. Yohai
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Editor information

Editors and Affiliations

  1. Fachbereich Mathematik, Universität Frankfurt, Robert Mayer Str. 6-10, 6000, Frankfurt, Germany

    Jürgen Franke  & Wolfgang Härdle  & 

  2. Department of Statistics, GN-22, University of Washington, Seattle, WA, 98195, USA

    Douglas Martin

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© 1984 Springer-Verlag Berlin Heidelberg

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Rousseeuw, P., Yohai, V. (1984). Robust Regression by Means of S-Estimators. In: Franke, J., Härdle, W., Martin, D. (eds) Robust and Nonlinear Time Series Analysis. Lecture Notes in Statistics, vol 26. Springer, New York, NY. https://doi.org/10.1007/978-1-4615-7821-5_15

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  • DOI: https://doi.org/10.1007/978-1-4615-7821-5_15

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-96102-6

  • Online ISBN: 978-1-4615-7821-5

  • eBook Packages: Springer Book Archive

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