Power Studies For Two-sample Methods For Multivariate Data

TL;DR

This paper evaluates the power of various non-parametric two-sample tests for multivariate data through extensive simulations, highlighting that no single test is universally best but proposing a small set of robust methods.

Contribution

It provides comprehensive simulation results for multiple tests and recommends a small set of methods with reliable power across different scenarios.

Findings

No single test is best for all cases.

A small set of methods covers most scenarios effectively.

Simulations include both continuous and discrete data.

Abstract

We present the results of a large number of simulation studies regarding the power of various non-parametric two-sample tests for multivariate data. This includes both continuous and discrete data. In general no single method can be relied upon to provide good power, any one method may be quite good for some combination of null hypothesis and alternative and may fail badly for another. Based on the results of these studies we propose a fairly small number of methods chosen such that for any of the case studies included here at least one of the methods has good power. The studies were carried out using the R package MD2sample, available from CRAN.