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Bayesian Approaches to Outliers and Robustness

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Specifying Statistical Models

Part of the book series: Lecture Notes in Statistics ((LNS,volume 16))

Abstract

A general, Bayesian approach to robustification via model elaboration is introduced and discussed. The approach is illustrated by considering the elaboration of standard models to incorporate the possibility of non-standard distributional shapes or of individual aberrant observations (outliers). Influence functions are then considered from a Bayesian point of view and an approach to robust time series analysis is outlined.

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Nottingham, UK

    A. F. M. Smith

Authors
  1. A. F. M. Smith
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Editor information

Editors and Affiliations

  1. Université d’Aix-Marseille II, France

    J. P. Florens

  2. C.O.R.E., Université Catholique de Louvain, Belgium

    M. Mouchart

  3. Université de Rouen, France

    J. P. Raoult

  4. Facultés Universitaires Saint-Louis, Bruxelles, Belgium

    L. Simar

  5. University of Nottingham, UK

    A. F. M. Smith

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© 1983 Springer-Verlag New York Inc.

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Smith, A.F.M. (1983). Bayesian Approaches to Outliers and Robustness. In: Florens, J.P., Mouchart, M., Raoult, J.P., Simar, L., Smith, A.F.M. (eds) Specifying Statistical Models. Lecture Notes in Statistics, vol 16. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-5503-1_2

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  • DOI: https://doi.org/10.1007/978-1-4612-5503-1_2

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-90809-0

  • Online ISBN: 978-1-4612-5503-1

  • eBook Packages: Springer Book Archive

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