METHODS FOR ESTIMATING PRINCIPAL POINTS

DOI10.1081/SAC-120003338zbMath1081.62538MaRDI QIDQ4416916

Ernst Stadlober, Erwin Stampfer

Publication date: 5 August 2003

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

zbMATH Keywords

Bootstrap methods\(k\)-means clusteringSelf-consistency algorithmsUnivariate distributions

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10)

Related Items

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, A parametric \(k\)-means algorithm, Principal points for an allometric extension model, High precision numerical computation of principal points for univariate distributions

Uses Software

• R
• S-PLUS

Cites Work

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• Strong consistency of k-means clustering
• A central limit theorem for k-means clustering
• Asymptotic distributions for clustering criteria
• Self-consistency: A fundamental concept in statistics
• Principal points
• Note on Grouping
• Uniqueness of locally optimal quantizer for log-concave density and convex error weighting function
• An asymptotic result on principal points for univariate distributions
• Computer Generation of Random Variables Using the Ratio of Uniform Deviates
• Estimation of Principal Points