A Nonparametric Two-Sample Test for Homogeneity of Distributions in High Dimension
Technological advances have enabled us to collect a lot of complex data objects,where homogeneity structure among these objects is widely used in Statistics.However,the existing metrics of homogeneity are subject to some qualifications,such as assumptions about the moment and parameters.To overcome the limitation,this paper proposes a new homogeneity test for high-dimensional two populations.Based on the double expectation formula and the properties of characteristic functions,a new measure and its empirical version are constructed in high-dimensional cases.Fur-thermore,under suitable regular conditions,the large sample nature of the proposed test is established too,such as the tests proposed in this paper converge to a mixture of x2 distributions under the null hypothesis and a normal distribution under the al-ternative hypothesis.Meanwhile,Monte Carlo simulation results show that the new methods perform better than several existing test procedures for high-dimensional data.
Tests for homogeneitytwo-sample problemV-statisticpermutation procedurehigh-dimension