An independence test for functional variables based on kernel normalized cross-covariance operator
Statistics Theory
2022-11-11 v1 Statistics Theory
Abstract
We propose an independence test for random variables valued into metric spaces by using a test statistic obtained from appropriately centering and rescaling the squared Hilbert-Schmidt norm of the usual empirical estimator of normalized cross-covariance operator. We then get asymptotic normality of this statistic under independence hypothesis, so leading to a new test for independence of functional random variables. A simulation study that allows to compare the proposed test to existing ones is provided.
Cite
@article{arxiv.2211.05731,
title = {An independence test for functional variables based on kernel normalized cross-covariance operator},
author = {Terence Kevin Manfoumbi Djonguet and Guy Martial Nkiet},
journal= {arXiv preprint arXiv:2211.05731},
year = {2022}
}