Kernel density estimation on Riemannian manifolds

The estimation of the underlying probability density of n i.i.d. random objects on a compact Riemannian manifold without boundary is considered. The proposed methodology adapts the technique of kernel density estimation on Euclidean sample spaces to this nonEuclidean setting. Under sufficient regularity assumptions on the underlying density, L2 convergence rates are obtained.

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Year of publication:	2005
Authors:	Pelletier, Bruno
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 73.2005, 3, p. 297-304
Publisher:	Elsevier
Keywords:	Nonparametric density estimation Kernel density estimation Riemannian manifolds L2 convergence

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005053162