Empirical geometry of multivariate data: a deconvolution approach.

DOI10.1214/aos/1016218232zbMath1105.62345OpenAlexW2070595951MaRDI QIDQ1848790

Vladimir I. Koltchinskii

Publication date: 14 November 2002

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aos/1016218232

zbMATH Keywords

clustersmetric entropyentropy dimensiondeconvolving estimatorssupport of probability distribution

Mathematics Subject Classification ID

Multivariate analysis (62H99) Estimation in multivariate analysis (62H12) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items

Noisy discriminant analysis with boundary assumptions, Multiscale geometric methods for data sets. I: Multiscale SVD, noise and curvature., Uniform consistency in nonparametric mixture models, Recovering the homology of immersed manifolds, Deconvolution for the Wasserstein metric and geometric inference, Manifold estimation and singular deconvolution under Hausdorff loss, Unnamed Item, Nonparametric deconvolution of density estimation based on observed sums, Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model, Estimating the support of multivariate densities under measurement error, Dimension Estimation Using Random Connection Models, Support estimation via moment estimation in presence of noise

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