Conditional independence testing via weighted partial copulas and nearest neighbors
Abstract
This paper introduces the \textit{weighted partial copula} function for testing conditional independence. The proposed test procedure results from these two ingredients: (i) the test statistic is an explicit Cramer-von Mises transformation of the \textit{weighted partial copula}, (ii) the regions of rejection are computed using a bootstrap procedure which mimics conditional independence by generating samples from the product measure of the estimated conditional marginals. Under conditional independence, the weak convergence of the \textit{weighted partial copula proces}s is established when the marginals are estimated using a smoothed local linear estimator. Finally, an experimental section demonstrates that the proposed test has competitive power compared to recent state-of-the-art methods such as kernel-based test.
Cite
@article{arxiv.2006.12839,
title = {Conditional independence testing via weighted partial copulas and nearest neighbors},
author = {Pascal Bianchi and Kevin Elgui and François Portier},
journal= {arXiv preprint arXiv:2006.12839},
year = {2021}
}