Multivariate Combination-based Permutation Tests with Application to Ranking of Multivariate Populations

Abstract

The multivariate permutation tests based on the nonparametric combination - NPC methodology (Pesarin and Salmaso, 2010) provides an effective hypothesis testing solution to many complex real problems which are quite common in case of multivariate response for many applied research areas such as biomedical and engineering research (Corain and Salmaso, 2013). Despite in the literature permutation tests are mostly derived by means of heuristic arguments, their natural theoretical background must be referred to the sufficiency and conditionally principles of inference. In this work we review and discuss the theoretical and practical relevance of NPC tests showing their effectiveness and easiness-in-use to address the ranking problem of several multivariate populations (Arboretti et al., 2014). References 1. Arboretti Giancristofaro R., Bonnini S., Corain L., Salmaso L. (2014). A Permutation Approach for Ranking of Multivariate Populations with Applications on Morphological 2. Analysis of Cell Cultures, Journal of Multivariate Analysis, to appear. 2. Pesarin F., Salmaso L. (2010). Permutation Tests for Complex Data. Theory, Applications and Software. Wiley Series in Probability and Statistics. Wiley, Chichester. 3. Corain L., Salmaso L. (2013). Improving Power of Multivariate Combination-based Permutation Tests, Statistics and Computing, DOI: 10.1007/s11222-013-9426-0.