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June 1, 1974Journal of the American Statistical Association2,527 citations

The Influence Curve and its Role in Robust Estimation

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FHFrank R. Hampel

Key Points

  • This paper aims to explore the influence curve's role in understanding local robustness in estimation methods.
  • Analyzed first derivatives of estimators viewed as functionals.
  • Discussed classical estimators such as trimmed means, Winsorized means, and Huber-estimators.
  • Established relations between von Mises functionals, jackknife, and U-statistics.
  • Presented a theory of robust estimation near strict parametric models.
  • Included a table showcasing numerical robustness properties of various estimators.

Abstract

Abstract This paper treats essentially the first derivative of an estimator viewed as functional and the ways in which it can be used to study local robustness properties. A theory of robust estimation “near” strict parametric models is briefly sketched and applied to some classical situations. Relations between von Mises functionals, the jackknife and U-statistics are indicated. A number of classical and new estimators are discussed, including trimmed and Winsorized means, Huber-estimators, and more generally maximum likelihood and M-estimators. Finally, a table with some numerical robustness properties is given.

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Cite This Study

Frank R. Hampel (1974) studied this question.

synapsesocial.com/papers/69d75d53f44a16d01ef30909https://doi.org/10.1080/01621459.1974.10482962
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