A distribution-free two-sample run test applicable to high-dimensional data

被引:52
作者
Biswas, Munmun [1 ]
Mukhopadhyay, Minerva [2 ]
Ghosh, Anil K. [1 ]
机构
[1] Indian Stat Inst, Theoret Stat & Math Unit, Kolkata 700108, India
[2] Indian Stat Inst, Appl Stat Unit, Kolkata 700108, India
关键词
Distribution-free property; High-dimension; low-sample-size data; Shortest Hamiltonian path; Two-sample run test; MULTIVARIATE RANK-TESTS; GEOMETRIC REPRESENTATION; EQUALITY; SAMPLES;
D O I
10.1093/biomet/asu045
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
We propose a multivariate generalization of the univariate two-sample run test based on the shortest Hamiltonian path. The proposed test is distribution-free in finite samples. While most existing two-sample tests perform poorly or are even inapplicable to high-dimensional data, our test can be conveniently used in high-dimension, low-sample-size situations. We investigate its power when the sample size remains fixed and the dimension of the data grows to infinity. Simulated and real datasets demonstrate our method's superiority over existing nonparametric two-sample tests.
引用
收藏
页码:913 / 926
页数:14
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