Adaptive robust estimation and testing
- 1 November 2007
- journal article
- Published by Wiley in British Journal of Mathematical and Statistical Psychology
- Vol. 60 (2), 267-293
- https://doi.org/10.1348/000711005x63755
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.Keywords
This publication has 48 references indexed in Scilit:
- Modern Robust Data Analysis Methods: Measures of Central Tendency.Psychological Methods, 2003
- Contributions to adaptive estimationJournal of Applied Statistics, 1998
- A construction and appraisal of pooled trimmed-tstatisticsCommunications in Statistics - Theory and Methods, 1991
- Are Robust Estimators Really Necessary?Technometrics, 1982
- A Two-Sample Adaptive Distribution-Free TestJournal of the American Statistical Association, 1975
- Adaptive Robust Procedures: A Partial Review and Some Suggestions for Future Applications and TheoryJournal of the American Statistical Association, 1974
- The two-sample trimmed t for unequal population variancesBiometrika, 1974
- The Small Sample Behavior of Some Statistics Which Test the Equality of Several MeansTechnometrics, 1974
- The Significance of the Difference Between Two Means when the Population Variances are UnequalBiometrika, 1938