Robust smoothed canonical correlation analysis for functional data
Statistics Theory
2020-11-24 v1 Methodology
Statistics Theory
Abstract
This paper provides robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by using robust association and scale measures combined with basis expansion and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent.
Cite
@article{arxiv.2011.10576,
title = {Robust smoothed canonical correlation analysis for functional data},
author = {Graciela Boente and Nadia Kudraszow},
journal= {arXiv preprint arXiv:2011.10576},
year = {2020}
}