Multi-aspect procedures for paired data with application to biometric morphing

Overview & Synopsis

According to Fisher, different tests of significance are appropriate to test different features of the same null hypothesis, thus leading to the Multi-Aspect (MA) testing issue. When dealing with paired data, usually inferences con- cern differences between the means. However, there are some circumstances in which it is of interest to test for differences between the variances. Here we present a nonparametric permutation solution to this problem. Our goal is to develop MA techniques for paired data, thus finding powerful tests, such that both differences in mean and in variance are separately identified.