METHODS FOR ESTIMATING PRINCIPAL POINTS
Autor: | Erwin Stampfer, Ernst Stadlober |
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Rok vydání: | 2002 |
Předmět: | |
Zdroj: | Communications in Statistics - Simulation and Computation. 31:261-277 |
ISSN: | 1532-4141 0361-0918 |
Popis: | Principal points of a distribution have been introduced by Flury [1] who tackled the problem of optimal grouping in multivariate data. In essence, principal points are the theoretical counterparts of cluster means obtained by a k-means clustering algorithm. There has been considerable effort to find efficient estimation procedures for principal points. It is well known that under certain conditions the k-means estimator is a consistent and asymptotically normal estimator of the population principal points. In this paper some material on principal points is reviewed and new algorithms for the estimation of principal points in univariate distributions (univariate principal points) are proposed. Additionally, the Bootstrap approach is applied to assess the variability of the suggested estimators. |
Databáze: | OpenAIRE |
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