A Nonparametric Approach to High-dimensional k-sample Comparison Problems
This provides a new method for statisticians and data scientists dealing with high-dimensional data comparison, though it appears incremental as it builds on existing nonparametric approaches with novel connections.
The authors tackled the problem of high-dimensional k-sample comparison by developing a nonparametric, distribution-free test based on spectral graph theory, which demonstrated strong performance across a variety of realistic scenarios.
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.