Log In Sign Up

A new nonparametric test for two sample multivariate location problem with application to astronomy

by   Soumita Modak, et al.

This paper provides a nonparametric test for the identity of two multivariate continuous distribution functions (d.f.'s) when they differ in locations. The test uses Wilcoxon rank-sum statistics on distances between observations for each of the components and is unaffected by outliers. It is numerically compared with two existing procedures in terms of power. The simulation study shows that its power is strictly increasing in the sample sizes and/or in the number of components. The applicability of this test is demonstrated by use of two astronomical data sets on early-type galaxies.


page 1

page 2

page 3

page 4


Investigating spatial scan statistics for multivariate functional data

This paper introduces new scan statistics for multivariate functional da...

A rank-based Cramér-von-Mises-type test for two samples

We study a rank based univariate two-sample distribution-free test. The ...

Two-sample nonparametric test for proportional reversed hazards

Several works have been undertaken in the context of proportional revers...

Efficiency Lower Bounds for Distribution-Free Hotelling-Type Two-Sample Tests Based on Optimal Transport

The Wilcoxon rank-sum test is one of the most popular distribution-free ...

Efficient unimodality test in clustering by signature testing

This paper provides a new unimodality test with application in hierarchi...

An automatic procedure to determine groups of nonparametric regression curves

In many situations it could be interesting to ascertain whether nonparam...

Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence

The need to test whether two random vectors are independent has spawned ...