Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)
Nicolas Vayatis, Marine Depecker, Stéphan Clémençcon
The purpose of the paper is to explore the connection between multivariate homogeneity tests and $\auc$ optimization. The latter problem has recently received much attention in the statistical learning literature. From the elementary observation that, in the two-sample problem setup, the null assumption corresponds to the situation where the area under the optimal ROC curve is equal to 1/2, we propose a two-stage testing method based on data splitting. A nearly optimal scoring function in the AUC sense is first learnt from one of the two half-samples. Data from the remaining half-sample are then projected onto the real line and eventually ranked according to the scoring function computed at the first stage. The last step amounts to performing a standard Mann-Whitney Wilcoxon test in the one-dimensional framework. We show that the learning step of the procedure does not affect the consistency of the test as well as its properties in terms of power, provided the ranking produced is accurate enough in the AUC sense. The results of a numerical experiment are eventually displayed in order to show the efficiency of the method.