A permutation approach for ranking of multivariate populations

The subject of this paper is to introduce a novel permutation-based nonparametric approach for the problem of ranking several multivariate populations with respect to both experimental and observation studies to be referred to the most useful design such as MANOVA (multivariate independent samples) and MRCB (multivariate randomized complete block design, i.e. multivariate dependent samples also known as repeated measures). This topic is not only of theoretical interest but also have a practical relevance, especially to business and industrial research where a reliable global ranking in terms of performance of all investigated products/prototypes is a very natural goal. In fact, the need to define an appropriate ranking of items (products, services, teaching courses, degree programs, and so on) is very common in both experimental and observational studies within the areas of business and industrial research.