A permutation approach for ranking of multivariate populations

TL;DR

This paper introduces a new permutation-based nonparametric method for ranking multivariate populations, applicable to various experimental designs, with practical relevance in business and industrial research for evaluating product and service performance.

Contribution

It presents a novel permutation approach for ranking multivariate populations in both independent and dependent sample settings, extending existing nonparametric methods.

Findings

Provides a reliable global ranking method for multivariate data

Applicable to MANOVA and MRCB designs in practice

Enhances decision-making in business and industrial research

Abstract

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.