10 citations · 25 across the 15 of their papers we have counts for
7 papers · 1 filter
Quasi-randomization-based inference for multivariate Mann-Whitney effects under random missingness
Dennis Dobler, Jörg-Tobias Kuhn, Lubna Amro +1
Marginal Mann-Whitney effects are widely used across various fields of research, and extensions of this estimand have been developed in many directions in statistical methodology.…
Covariance Correction for Permutation Statistics in Multiple Testing Problems
Merle Munko, Paavo Sattler
In qualitative statistics, permutation tests are very popular, mainly because of their finite-sample exactness under exchangeability. However, in non-exchangeable settings, the cov…
Multivariate and Multiple Contrast Testing in General Covariate-adjusted Factorial Designs
Marléne Baumeister, Konstantin Emil Thiel, Lynn Matits +3
Evaluating intervention effects on multiple outcomes is a central research goal in a wide range of quantitative sciences. It is thereby common to compare interventions among each o…
Resampling NANCOVA: Nonparametric Analysis of Covariance in Small Samples
Konstantin Emil Thiel, Paavo Sattler, Arne C Bathke +1
Analysis of covariance is a crucial method for improving precision of statistical tests for factor effects in randomized experiments. However, existing solutions suffer from one or…
Quadratic Form based Multiple Contrast Tests for Comparison of Group Means
Paavo Sattler, Markus Pauly, Merle Munko
Comparing the mean vectors across different groups is a cornerstone in the realm of multivariate statistics, with quadratic forms commonly serving as test statistics. However, when…
Choice of the hypothesis matrix for using the Anova-type-statistic
Paavo Sattler, Manuel Rosenbaum
Initially developed in Brunner et al. (1997), the Anova-type-statistic (ATS) is one of the most used quadratic forms for testing multivariate hypotheses for a variety of different…
Testing for patterns and structures in covariance and correlation matrices
Paavo Sattler, Dennis Dobler
Covariance matrices of random vectors contain information that is crucial for modelling. Specific structures and patterns of the covariances (or correlations) may be used to justif…