title: Adaptive robust estimation and testing.
abstract: 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.
authors.affiliation: Department of Psychology, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2. hj_keselman@umanitoba.ca
authors.forename: H J
authors.initials: HJ
authors.lastname: Keselman
authors.forename: Rand R
authors.initials: RR
authors.lastname: Wilcox
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Lix
authors.forename: James
authors.initials: J
authors.lastname: Algina
authors.forename: Katherine
authors.initials: K
authors.lastname: Fradette
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*