A geometric framework for asymptotic inference of principal subspaces in PCA

In this article, we develop an asymptotic method for testing hypothesis on the set of all linear subspaces arising from PCA and for constructing confidence regions for this set. This procedure is derived from intrinsic estimation in each Grassmannian, endowed with a structure of Riemannian manifold, to which each of these subspaces belong.