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Are Robust Estimators Really Necessary?
David M. Rocke, George W. Downs and Alan J. Rocke
Vol. 24, No. 2 (May, 1982), pp. 95-101
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/1268485
Page Count: 7
You can always find the topics here!Topics: Datasets, Statistical discrepancies, Estimators for the mean, Estimators, Statistical estimation, Estimation methods, Analytical estimating, Chemistry, Estimation bias, Population estimates
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Although there is substantial literature on robust estimation, most scientists continue to employ traditional methods. They remain skeptical about the practical benefit of employing robust techniques and doubt the realism of the long-tailed error distributions commonly employed by their proponents in Monte Carlo studies. In this article a method of comparing the performance of estimators of location is developed and applied to a series of historical data sets in the physical sciences and to a collection of modern analytical-chemistry data sets. Both sets of results suggest that either severely trimmed means or modern robust estimators are required for optimal efficiency.
Technometrics © 1982 American Statistical Association