Large sample theory of intrinsic and extrinsic sample means on manifolds-ii

Rabi Bhattacharya, Vic Patrangenaru

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


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 S d (directional spaces), real projective space ℝP N-1 (axial spaces), complex projective space ℂP k-2 (planar shape spaces) w.r.t. Veronese-Whitney embeddings and a three-dimensional shape space ∑ 3 4.

Original languageEnglish (US)
Pages (from-to)1225-1259
Number of pages35
JournalAnnals of Statistics
Issue number3
StatePublished - Jun 2005


  • Bootstrapping
  • Central limit theorem
  • Confidence regions
  • Extrinsic mean
  • Fréchet mean

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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