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Title: General MANOVA with missing data: A resampling-based solution Authors:  Lubna Amro - TU Dortmund University (Germany) [presenting]
Markus Pauly - Technical University of Dortmund (Germany)
Burim Ramosaj - TU Dortmund University (Germany)
Abstract: Repeated measure designs and split plot plans are widely employed in scientific and medical research. The analysis of such designs is typically based on MANOVA models, requiring complete data, and certain assumptions on the underlying parametric distribution, such as normality or covariance matrix homogeneity. Several nonparametric multivariate methods have been proposed in the literature. They overcome the distributional assumptions, but the issue of missing data remains. The aim is to develop asymptotic correct procedures that are capable of handling missing values without assuming normality and allowing for covariance matrices that are heterogeneous between groups. This is achieved by applying a proper resampling method in combination with quadratic form-type test statistics. An extensive simulation study is conducted, exemplifying the tests for finite sample sizes under different missingness mechanisms. Finally, an illustrative data example is analyzed.