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Bayesian Robust Estimation of the Mean

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  • Wesley Johnson
  • Jessica Utts
  • Larry Pearson

Abstract

We indicate the applicability of a Bayesian method of estimating a mean in the face of mean‐shift contamination. Simple approximate Bayesian “confidence interval” formulas are obtained by approximating the posterior distribution. Several examples facilitate the comparison of the true and approximate posterior distributions. Performances of point estimators are assessed for the historical data sets presented by Stigler (1977). Empirical influence curves comparing the Bayesian estimators with other robust estimators are given. We conclude that the Bayesian estimators are competitive with some of the “best” robust estimators in the literature.

Suggested Citation

  • Wesley Johnson & Jessica Utts & Larry Pearson, 1986. "Bayesian Robust Estimation of the Mean," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(1), pages 63-72, March.
    • Handle: RePEc:bla:jorssc:v:35:y:1986:i:1:p:63-72
    DOI: 10.2307/2347866
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