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Some Adaptive Robust Estimators which Work with Real Data
Author(s) -
Hill N. J.,
Padmanabhan A. R.
Publication year - 1991
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710330111
Subject(s) - estimator , robust statistics , mathematics , statistics , variance (accounting) , m estimator , computer science , accounting , business
An adaptive R ‐estimator θ A and an adaptive trimmed mean MAT are proposed. The performance of these and a number of other robust estimators are studied on real data sets, drawn from the astronomical, behavioural, biomedical, chemical, engineering and physical sciences. In the case of sets that can be assumed to have come from symmetric distributions, the best performer is θ A . The next best performers are the Hodges‐Lehmann estimator, Bisquare (7.5) and Huber (1.5), in that order. MAT works well with all kinds of sets–symmetric or skewed. Extensions of these results to ANOVA and regression models are mentioned.