Self-consistent patterns for symmetric multivariate distributions

DOI10.1007/S003579900020zbMath0902.62057OpenAlexW1978291652WikidataQ127883554 ScholiaQ127883554MaRDI QIDQ1393052

Thaddeus Tarpey

Publication date: 9 December 1998

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s003579900020

zbMATH Keywords

mean squared errorprincipal componentsVoronoi regionsprincipal pointsspherical distributionselliptical distributionk-means clusteringself-consistent points

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Nonparametric estimation (62G05) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

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Principal points and elliptical distributions from the multivariate setting to the functional case ⋮ Optimal partitioning for the proportional hazards model ⋮ Representative points for location-biased datasets ⋮ A principal subspace theorem for 2-principal points of general location mixtures of spherically symmetric distributions ⋮ Principal points of a multivariate mixture distribution ⋮ Optimal estimators of principal points for minimizing expected mean squared distance ⋮ Optimal principal points estimators of multivariate distributions of location-scale and location-scale-rotation families ⋮ Self-consistency and a generalized principal subspace theorem ⋮ A parametric \(k\)-means algorithm ⋮ Model misspecification ⋮ High precision numerical computation of principal points for univariate distributions ⋮ Three kinds of discrete approximations of statistical multivariate distributions and their applications ⋮ Principal point classification: applications to differentiating drug and placebo responses in longitudinal studies ⋮ Parallel principal axes

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