A geometric framework for asymptotic inference of principal subspaces in PCA
Statistics Theory
2024-11-06 v3 Statistics Theory
Abstract
In this article, we develop an asymptotic method for constructing confidence regions for the set of all linear subspaces arising from PCA, from which we derive hypothesis tests on this set. Our method is based on the geometry of Riemannian manifolds with which some sets of linear subspaces are endowed.
Keywords
Cite
@article{arxiv.2209.02025,
title = {A geometric framework for asymptotic inference of principal subspaces in PCA},
author = {Dimbihery Rabenoro and Xavier Pennec},
journal= {arXiv preprint arXiv:2209.02025},
year = {2024}
}