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B0509
Title: Wild bootstraping rank-based procedures: Factorial designs and multiple testing Authors:  Maria Umlauft - Ulm University (Germany) [presenting]
Frank Konietschke - Charite Berlin (Germany)
Markus Pauly - University of Ulm (Germany)
Abstract: Scientific experiments comparing more than two groups are usually inferred by a classical ANOVA model. If the global null hypothesis of no treatment effect is rejected, multiple comparisons between the groups are usually performed, e.g. testing all pairwise hypothesis by means of unpaired $t$-test type Statistics and/or adequate simultaneous confidence intervals for the corresponding treatment effect. However, the underlying distributional assumptions (such as normality and variance homogeneity) are often not met in real data. Furthermore, the used effect sizes (mean differences or more general mean contrasts) may not be appropriate measures, especially if ordinal or ordered categorical data are present. To this end, several nonparametric procedures for simultaneous inference in general factorial designs have been studied. Here, the current approaches do not lead to simultaneous confidence intervals for contrasts in adequate effect measures. Thus, global inference and multiple testing procedures for an adequate nonparametric effect measure are required. We discuss rank-based multiple comparison procedures which can be used to test hypotheses formulated in terms of purely non-parametric treatment effects. In particular, different resampling methods as small sample size approximations will be discussed.