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Title: Multiplication-combination tests for incomplete paired data Authors:  Lubna Amro - Institute of Statistics, Ulm University (Germany) [presenting]
Markus Pauly - University of Ulm (Germany)
Frank Konietschke - University of Texas at Dallas (United States)
Abstract: Statistical procedures are considered for hypothesis testing of real valued functionals of matched pairs with missing values. In order to improve the accuracy of existing methods, we propose a novel multiplication combination procedure. Dividing the observed data into dependent (completely observed) pairs and independent (incompletely observed) components, it is based on combining separate results of adequate tests for the two sub datasets. The methods can be applied for parametric as well as semi- and nonparametric models and make efficient use of all available data. In particular, the approaches are flexible and can be used to test different hypotheses in various models of interest. This is exemplified by a detailed study of mean- as well as rank-based approaches. Extensive simulations show that the proposed procedures are more accurate than existing competitors. A real data set illustrates the application of the methods.