Improving Cramér-Rao Bound And Its Variants: An Extrinsic Geometry Perspective
This paper presents a geometric refinement of the classical Cramér-Rao bound in the non-asymptotic regime by leveraging the extrinsic curvature of the statistical model manifold's square root embedding to derive tighter, curvature-aware lower bounds on estimator variance.