Convergence of Hessian estimator from random samples on a manifold with boundary

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Hauptverfasser: Chen, Chih-Wei, Wu, Hau-Tieng
Format: Preprint
Veröffentlicht: 2023
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author Chen, Chih-Wei
Wu, Hau-Tieng
author_facet Chen, Chih-Wei
Wu, Hau-Tieng
contents A common method for estimating the Hessian operator from random samples on a low-dimensional manifold involves locally fitting a quadratic polynomial. Although widely used, it is unclear if this estimator introduces bias, especially in complex manifolds with boundaries and nonuniform sampling. Rigorous theoretical guarantees of its asymptotic behavior have been lacking. We show that, under mild conditions, this estimator asymptotically converges to the Hessian operator, with nonuniform sampling and curvature effects proving negligible, even near boundaries. Our analysis framework simplifies the intensive computations required for direct analysis.
format Preprint
id arxiv_https___arxiv_org_abs_2303_12547
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Convergence of Hessian estimator from random samples on a manifold with boundary
Chen, Chih-Wei
Wu, Hau-Tieng
Statistics Theory
Differential Geometry
62-07
A common method for estimating the Hessian operator from random samples on a low-dimensional manifold involves locally fitting a quadratic polynomial. Although widely used, it is unclear if this estimator introduces bias, especially in complex manifolds with boundaries and nonuniform sampling. Rigorous theoretical guarantees of its asymptotic behavior have been lacking. We show that, under mild conditions, this estimator asymptotically converges to the Hessian operator, with nonuniform sampling and curvature effects proving negligible, even near boundaries. Our analysis framework simplifies the intensive computations required for direct analysis.
title Convergence of Hessian estimator from random samples on a manifold with boundary
topic Statistics Theory
Differential Geometry
62-07
url https://arxiv.org/abs/2303.12547