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A remark on robustness and weak continuity of M-estimators

Published online by Cambridge University Press:  09 April 2009

Brenton R. Clarke
Affiliation:
Mathematics and Statistics Division of Science and Engineering Murdoch UniversityMurdoch, WA 6150Australia e-mail: clarke@prodigal.murdoch.edu.au
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Abstract

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Global weak continuity of M-functionals in a neighbourhood of the parametric distribution is established. This has implications for robustness of M-estimators vis a vis definitions put forward by Hampel. For instance the Tukey bisquare location estimator is robust on neighbourhoods of the parametric model, but the median is not.

Type
Research Article
Copyright
Copyright © Australian Mathematical Society 2000

References

[1]Beaton, A. E. and Tukey, J. W., ‘The fitting of power series meaning polynomials, illustrated on band-spectroscopic data’, Technometrics 16 (1974), 147185.CrossRefGoogle Scholar
[2]Bednarski, T. and Zontek, S., ‘Robust estimation of parameters in a mixed unbalanced model’, Ann. Statist. 24 (1996), 14931510.CrossRefGoogle Scholar
[3]Clarke, B. R., ‘Uniqueness and Fréchet differentiability of functional solutions to maximum likelihood type equations’, Ann. Statist. 11 (1983), 11961205.CrossRefGoogle Scholar
[4]Clarke, B. R., ‘The selection functional’, Probab. Math. Statist. 11 (1991), 149156.Google Scholar
[5]Clarke, B. R. and Heathcote, C. R., ‘Robust estimation of k-component univariate normal mixtures’, Ann. Inst. Statist. Math. 46 (1994), 8393.CrossRefGoogle Scholar
[6]Hampel, F. R., ‘A general qualitative definition of robustness’, Ann. Math. Statist. 42 (1971), 18871896.CrossRefGoogle Scholar
[7]Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A., Robust statistics: the approach based on influence functions (Wiley, New York, 1986).Google Scholar
[8]Heathcote, C. R. and Silvapulle, M. J., ‘Minimum mean squared estimation of location and scale parameters under misspecification of the model’, Biometrika 68 (1981), 501514.CrossRefGoogle Scholar
[9]Huggins, R. M., ‘On the robust analysis of variance components models for pedigree data’, Austral. J. Statist. 35 (1993), 4357.CrossRefGoogle Scholar
[10]Prohorov, Y. V., ‘Convergence of random processes and limit theorems in probability’, Theory Probab. Appl. 1 (1956), 157214.CrossRefGoogle Scholar
[11]Varadarajan, V. S., ‘On the convergence of probability distributions’, Sankhyā 19 (1958), 2326.Google Scholar
[12]von Mises, R., ‘On the asymptotic distribution of differentiable statistical functions’, Ann. Math. Statist. 18 (1947), 309348.CrossRefGoogle Scholar