TITLE: Finite-sample inference for intrinsic dimension via Gaussian kernels

ABSTRACT: We consider intrinsic-dimension estimation for data supported on an unknown d-dimensional submanifold in Euclidean space. We first introduce key concepts and existing approaches, then present new finite-sample concentration bounds for Gaussian-kernel estimators under mild regularity assumptions on the manifold and sampling distribution. We also discuss data-driven bandwidth selection methods.