A Nonparametric Approach to High-dimensional k-sample Comparison Problems
Methodology
2019-08-12 v2 Statistics Theory
Machine Learning
Statistics Theory
Abstract
High-dimensional k-sample comparison is a common applied problem. We construct a class of easy-to-implement nonparametric distribution-free tests based on new tools and unexplored connections with spectral graph theory. The test is shown to possess various desirable properties along with a characteristic exploratory flavor that has practical consequences. The numerical examples show that our method works surprisingly well under a broad range of realistic situations.
Cite
@article{arxiv.1810.01724,
title = {A Nonparametric Approach to High-dimensional k-sample Comparison Problems},
author = {Subhadeep and Mukhopadhyay and Kaijun Wang},
journal= {arXiv preprint arXiv:1810.01724},
year = {2019}
}
Comments
Biometrika (in press)