  We consider statistical procedures 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. Our
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 apporaches. Extensive simulations show that the proposed procedures
are more accurate than existing competitors. A real data set illustrates the
application of the methods.
