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Breakthroughs in Statistics pp 492–518Cite as

Robust Estimation of a Location Parameter

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Abstract

This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators—intermediaries between sample mean and sample median—that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators. For the general background, see Tukey (1960) (p. 448 ff.)

Keywords

  • Maximum Likelihood Estimator
  • Location Parameter
  • Robust Estimation
  • Asymptotic Normality
  • Asymptotic Variance

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

1This research was performed while the author held an Adolph C. and Mary Sprague Miller Fellowship.

2 Now at Cornell University.

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Author information

Authors and Affiliations

  1. University of California, Berkeley, USA

    Peter J. Huber

Authors
  1. Peter J. Huber
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Editor information

Editors and Affiliations

  1. College of Business and Management, University of Maryland at College Park, 20742, College Park, MD, USA

    Samuel Kotz

  2. Department of Statistics Phillips Hall, The University of North Carolina at Chapel Hill, 27599, Chapel Hill, NC, USA

    Norman L. Johnson

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© 1992 Springer-Verlag New York, Inc.

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Huber, P.J. (1992). Robust Estimation of a Location Parameter. In: Kotz, S., Johnson, N.L. (eds) Breakthroughs in Statistics. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4380-9_35

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  • DOI: https://doi.org/10.1007/978-1-4612-4380-9_35

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94039-7

  • Online ISBN: 978-1-4612-4380-9

  • eBook Packages: Springer Book Archive

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