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Industrial Statistics pp 165–174Cite as

Robust Inference and Experimental Design for Multi-Factor Models

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Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

For multi-factor linear models outlier robust estimators and tests are derived. As robustness criteria the finite sample breakdown point and the asymptotic bias in shrinking contamination neighbourhoods are used. It is shown that one-step M-estimators can combine high breakdown point, small asymptotic bias and high efficiency. For tests similar results can be shown by basing the tests on one-step M-estimaters. It turns out that highest efficiency can be achieved if appropriate designs are used.

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Author information

Authors and Affiliations

  1. Fachbereich Mathematik und Informatik, Freie Universität Berlin, WE 1 Arnimallee 2-6, D - 14195, Berlin, Germany

    Christine H. Müller

Authors
  1. Christine H. Müller
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Editor information

Editors and Affiliations

  1. Department of Statistics, Athens University of Economics and Business, 76 Patission Street, 104 34, Athens, Greece

    Christos P. Kitsos

  2. German Cancer Research Center, Biostatistics Unit, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany

    Lutz Edler

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© 1997 Springer-Verlag Berlin Heidelberg

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Müller, C.H. (1997). Robust Inference and Experimental Design for Multi-Factor Models. In: Kitsos, C.P., Edler, L. (eds) Industrial Statistics. Contributions to Statistics. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-59268-3_13

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  • DOI: https://doi.org/10.1007/978-3-642-59268-3_13

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1042-4

  • Online ISBN: 978-3-642-59268-3

  • eBook Packages: Springer Book Archive

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