Advances on Permutation Multivariate Analysis of Variance for big data

Abstract In many applications of the multivariate analyses of variance, the classic parametric solutions for testing hypotheses of equality in population means or multisample and multivariate location problems might not be suitable for various reasons. Multivariate multisample location problems lack a comparative study of the power behaviour of the most important combined permutation tests as the number of variables diverges. In particular, it is useful to know under which conditions each of the different tests is preferable in terms of power, how the power of each test increases when the number of variables under the alternative hypothesis diverges, and the power behaviour of each test as the function of the proportion of true alternative hypotheses. The purpose of this paper is to fill the gap in the literature about combined permutation tests, in particular for big data with a large number of variables. A Monte Carlo simulation study was carried out to investigate the power behaviour of the tests, and the application to a real case study was performed to show the utility of the method.