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

• 成果类型: Article
• 署名作者: Biswas, Munmun; Mukhopadhyay, Minerva; Ghosh, Anil K.
• 署名单位: Indian Statistical Institute; Indian Statistical Institute Kolkata; Indian Statistical Institute; Indian Statistical Institute Kolkata
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu045
• 发表日期: 2014
• 页码: 913926
• 关键词: multivariate rank-tests; geometric representation; EQUALITY; samples

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.