METHODS FOR ESTIMATING PRINCIPAL POINTS

Autor:	Erwin Stampfer, Ernst Stadlober
Rok vydání:	2002
Předmět:	Statistics and Probability education.field_of_study Estimation theory Population Univariate k-means clustering Estimator Multivariate normal distribution Cardinal point Modeling and Simulation Statistics Cluster analysis education Mathematics
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
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::40197af68b60300ec4796bf73d4dbaab https://doi.org/10.1081/sac-120003338 Zobrazit plný text záznamu