The Annals of Statistics

Large sample theory of intrinsic and extrinsic sample means on manifolds—II

Rabi Bhattacharya and Vic Patrangenaru

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This article develops nonparametric inference procedures for estimation and testing problems for means on manifolds. A central limit theorem for Fréchet sample means is derived leading to an asymptotic distribution theory of intrinsic sample means on Riemannian manifolds. Central limit theorems are also obtained for extrinsic sample means w.r.t. an arbitrary embedding of a differentiable manifold in a Euclidean space. Bootstrap methods particularly suitable for these problems are presented. Applications are given to distributions on the sphere Sd (directional spaces), real projective space ℝPN−1 (axial spaces), complex projective space ℂPk−2 (planar shape spaces) w.r.t. Veronese–Whitney embeddings and a three-dimensional shape space Σ34.

Article information

Ann. Statist. Volume 33, Number 3 (2005), 1225-1259.

First available in Project Euclid: 1 July 2005

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H11: Directional data; spatial statistics
Secondary: 62H10: Distribution of statistics

Fréchet mean extrinsic mean central limit theorem confidence regions bootstrapping


Bhattacharya, Rabi; Patrangenaru, Vic. Large sample theory of intrinsic and extrinsic sample means on manifolds—II. Ann. Statist. 33 (2005), no. 3, 1225--1259. doi:10.1214/009053605000000093.

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