6th CEN Conference

Session

IS2: Multiple tests beyond parametric assumptions

19 May 2026, 13:45

Room 1 A

Conveners

IS2: Multiple tests beyond parametric assumptions

• Paavo Sattler (RWTH Aachen University)

6. Multiple Comparison Procedures for Simultaneous Inference in General Factorial Design for Multivariate Functional Data under Non-parametric Assumptions

Łukasz Smaga (Adam Mickiewicz University)

19/05/2026, 13:45

invited lecture

Functional Data Analysis (FDA), focusing on data composed of functions or curves, has become increasingly popular. We study reliable methods for comparing multiple groups of functional data, especially in studies involving several factors or complex designs. We introduce a new statistical approach designed for multivariate functional data. Our methods are reliable because they allow us to...

41. From group-sequential to flexible adaptive designs for time-to-event endpoints: Assumptions for valid multiplicity control

Moritz Fabian Danzer (University of Münster)

19/05/2026, 14:15

oral presentation

Adaptive and, in particular, group-sequential designs are well-established in clinical trials. Time-to-event endpoints pose particular challenges because individual participants can contribute data to multiple stages of the trial. Nevertheless, the log-rank test - the standard analysis method for time-to-event data - can be embedded in flexible adaptive designs (e.g. with sample-size...

51. Wild bootstrapping the (asymptotic) joint distribution of rank-based statistics

Lukas Mödl (Institut für Biometrie und Klinische Epidemiologie, Charité -- Universitätsmedizin Berlin)

19/05/2026, 14:35

oral presentation

Quadratic forms, such as the rank-based Wald-type statistic or the rank-based ANOVA-type statistic, are widely used to compare multivariate distributions without the necessity of parametric assumptions (like multivariate normality). These tests have two major limitations, however:
i) They are, by construction, omnibus tests and thus not able to locate which specific dimensions (variables) are...

15. Beyond Independence: A Unified Approach to the Multiple Nonparametric Behrens-Fisher Problem

Erin Sprünken (Charité - Universitätsmedizin Berlin)

19/05/2026, 14:55

oral presentation

In many trials and experiments, subjects are not only observed once, but multiple times, resulting in a cluster of possibly correlated observations. For example, mice sharing the same cage or students of the same class are typical examples of clustered data. Typically, under the assumption of normally distributed data, mixed models are used for analysis.
However, this model assumption is...