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Monte Carlo Investigation of Robust Methods

In: Probability and Statistical Inference

Author

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  • R. Dutter

    (Technical University Graz, Institute of Statistics)

  • I. Ganster

    (Technical University Graz, Institute of Statistics)

Abstract

The need and importance of statistical methods which should behave well not only at the supposed parametric model but also in a certain neighbourhood, are obvious. Such “robust” methods are often difficult to investigate analytically. Well-developed simulation techniques with high-speed computers enable us to examine quantitatively up to a certain degree the behaviour of such procedures. In this paper we describe some possibilities for the investigation of robust statistical methods by Monte Carlo techniques. As an illustration the examination of location estimators when different symmetric distributions (long- and short-tailed) are underlying, is reported. 62 estimators are considered where most of them are designed to guard against outliers in a certain way. Distributions are mainly taken from the exponential power family. Some analysis of the simulated mean square errors is also given.

Suggested Citation

  • R. Dutter & I. Ganster, 1982. "Monte Carlo Investigation of Robust Methods," Springer Books, in: Wilfried Grossmann & Georg Ch. Pflug & Wolfgang Wertz (ed.), Probability and Statistical Inference, pages 59-72, Springer.
    • Handle: RePEc:spr:sprchp:978-94-009-7840-9_8
    DOI: 10.1007/978-94-009-7840-9_8
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