Asymptotics for trimmed \(k\)-means and associated tolerance zones.

DOI10.1016/S0378-3758(98)00196-7zbMath1054.62551MaRDI QIDQ1298870

Alfonso Gordaliza, Carlos Matrán, Luis Angel García-Escudero

Publication date: 1999

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

robustnessclustering methodsdistribution freeness

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35) Nonparametric tolerance and confidence regions (62G15)

Related Items (2)

On the asymptotics of trimmed best \(k\)-nets ⋮ A central limit theorem for multivariate generalized trimmed \(k\)-means

Cites Work

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• On the breakdown point of multivariate location estimators based on trimming procedures
• The strong law of large numbers for k-means and best possible nets of Banach valued random variables
• Strong law of large numbers for measures of central tendency and dispersion of random variables in compact metric spaces
• Strong consistency of k-means clustering
• Nonparametric interval and point prediction using data trimmed by a Grubbs-type outlier rule
• A central limit theorem for k-means clustering
• Best approximations to random variables based on trimming procedures
• Asymptotic distributions for clustering criteria
• The asymptotics of the least trimmed absolute deviations (LTAD) estimator
• Trimmed \(k\)-means: An attempt to robustify quantizers
• Principal points and self-consistent points of elliptical distributions
• Uniqueness of principal points for univariate distributions
• On Grouping for Maximum Homogeneity
• Note on Grouping
• Nonparametric Statistical Data Modeling
• Generalized Means and Associated Families of Distributions
• Convergence of stochastic processes

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