A nonparametric two-sample hypothesis testing problem for random dot product graphs
Statistics Theory
2015-11-13 v2 Statistics Theory
Abstract
We consider the problem of testing whether two finite-dimensional random dot product graphs have generating latent positions that are independently drawn from the same distribution, or distributions that are related via scaling or projection. We propose a test statistic that is a kernel-based function of the adjacency spectral embedding for each graph. We obtain a limiting distribution for our test statistic under the null and we show that our test procedure is consistent across a broad range of alternatives.
Cite
@article{arxiv.1409.2344,
title = {A nonparametric two-sample hypothesis testing problem for random dot product graphs},
author = {Minh Tang and Avanti Athreya and Daniel L. Sussman and Vince Lyzinski and Carey E. Priebe},
journal= {arXiv preprint arXiv:1409.2344},
year = {2015}
}
Comments
24 pages, 1 figures