Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem

SOME HOHPARAMETRIC TESTS FOR THE MULTIVARIATE SEVERAL SAMPLE LOCATION PROBlEM

Robust nonparametric tests for the two-sample location problem

We construct and investigate robust nonparametric tests for the twosample location problem. A test based on a suitable scaling of the median of the set of differences between the two samples, which is the Hodges-Lehmann shift estimator corresponding to the Wilcoxon two-sample rank test, leads to higher robustness against outliers than the Wilcoxon test itself, while preserving its efficiency un...

Optimal Tests for the Two-Sample Spherical Location Problem

We tackle the classical two-sample spherical location problem for directional data by having recourse to the Le Cam methodology, habitually used in classical “linear” multivariate analysis. More precisely we construct locally and asymptotically optimal (in the maximin sense) parametric tests, which we then turn into semi-parametric ones in two distinct ways. First, by using a studentization arg...

Two Sample Tests for High - Dimensional Covariance Matrices

We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance–covariance matrices, and the other is on off-diagonal sub-matrices, which define the covariance between two nonoverlapping segments of the high-dimensional random vectors. The tests are applicable (i) when the data dimension is much larger than the sample size...

On group sequential tests based on robust location and scale estimators in the two-sample problem

The behaviour of group sequential tests in the two-sample problem is investigated if one replaces the classical non-robust estimators in the t-test statistic by modern robust estimators of location and scale. Hampel's 3-part redescending M-estimator 25A used in the Princeton study and the robust scale estimators length of the shortest half proposed by Rousseeuw & Leroy and Q proposed by Roussee...