Optimal stratification and clustering on the line using the \(L_ 1\)- norm
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Publication:1090035
DOI10.1016/0047-259X(86)90099-0zbMath0621.62061OpenAlexW1989289856MaRDI QIDQ1090035
Publication date: 1986
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0047-259x(86)90099-0
consistencyasymptotic normalityoptimal partitionasymptotic behaviourBrownian bridgeconfidence intervalclustersquantile processcentral limit theoryclass boundariescontinuous one-dimensional observationsL1-clusteringoptimal stratificationwithin group dispersion
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Central limit and other weak theorems (60F05) Nonparametric tolerance and confidence regions (62G15)
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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
- Strong approximations of the quantile process
- Sufficient conditions for uniqueness of a locally optimal quantizer for a class of convex error weighting functions
- Nonparametric Statistical Data Modeling
- Least squares quantization in PCM
- On the Identifiability of Finite Mixtures
- On Bahadur's Representation of Sample Quantiles