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A smoothing principle for the Huber and other location M-estimators

Author

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  • Hampel, Frank
  • Hennig, Christian
  • Ronchetti, Elvezio

Abstract

A smoothing principle for M-estimators is proposed. The smoothing depends on the sample size so that the resulting smoothed M-estimator coincides with the initial M-estimator when n-->[infinity]. The smoothing principle is motivated by an analysis of the requirements in the proof of the Cramér-Rao bound. The principle can be applied to every M-estimator. A simulation study is carried out where smoothed Huber, ML-, and Bisquare M-estimators are compared with their non-smoothed counterparts and with Pitman estimators on data generated from several distributions with and without estimated scale. This leads to encouraging results for the smoothed estimators, and particularly the smoothed Huber estimator, as they improve upon the initial M-estimators particularly in the tail areas of the distributions of the estimators. The results are backed up by small sample asymptotics.

Suggested Citation

  • Hampel, Frank & Hennig, Christian & Ronchetti, Elvezio, 2011. "A smoothing principle for the Huber and other location M-estimators," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 324-337, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:324-337
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    References listed on IDEAS

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    1. Seo, Byungtae & Lindsay, Bruce G., 2010. "A computational strategy for doubly smoothed MLE exemplified in the normal mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1930-1941, August.
    2. Rousseeuw, Peter J. & Verboven, Sabine, 2002. "Robust estimation in very small samples," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 741-758, October.
    3. Chen, Zhiqiang & E. Tyler, David, 2004. "On the finite sample breakdown points of redescending M-estimates of location," Statistics & Probability Letters, Elsevier, vol. 69(3), pages 233-242, September.
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    Cited by:

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    2. Wang, Xiaoguang & Shi, Xinyong, 2014. "Robust estimation for survival partially linear single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 140-152.
    3. Erxu Pi & Nitin Mantri & Sai Ming Ngai & Hongfei Lu & Liqun Du, 2013. "BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.

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