A Class of Combinations of Dependent Tests by a Resampling Procedure

A number of statistical hypotheses could be tested by combining k> 2 test statistics in a single statistic. e.g. hypotheses for the equality of several means and variances, or more generally for different effects jointly relevant for the analysis. In this paper, we show how resampling techniques, based on permutations of the data, may be conveniently used to combine the k test statistics, when they are characterized by an unknown dependence structure. These techniques have recently been interpreted (Hinkley (1989), Pesarin (1989) and Romano (1989)) as conditional bootstrap methods.