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Adaptive robust estimation and testing
Author(s) -
Keselman H. J.,
Wilcox Rand R.,
Lix Lisa M.,
Algina James,
Fradette Katherine
Publication year - 2007
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711005x63755
Subject(s) - statistics , normality , type i and type ii errors , trimming , mathematics , sample size determination , variance (accounting) , econometrics , null hypothesis , population , computer science , medicine , accounting , operating system , environmental health , business
We examined nine adaptive methods of trimming, that is, methods that empirically determine when data should be trimmed and the amount to be trimmed from the tails of the empirical distribution. Over the 240 empirical values collected for each method investigated, in which we varied the total percentage of data trimmed, sample size, degree of variance heterogeneity, pairing of variances and group sizes, and population shape, one method resulted in exceptionally good control of Type I errors. However, under less extreme cases of non‐normality and variance heterogeneity a number of methods exhibited reasonably good Type I error control. With regard to the power to detect non‐null treatment effects, we found that the choice among the methods depended on the degree of non‐normality and variance heterogeneity. Recommendations are offered.