A robust method for cluster analysis

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Let there be given a contaminated list of n R^d-valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with n-r outliers for the parameters of the g populations; it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled determinant criterion of cluster analysis. We also propose an efficient algorithm for approximating this estimator and study its breakdown points for mean values and pooled SSP matrix.
Published at http://dx.doi.org/10.1214/009053604000000940 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Statistics Theory, 62H30 (Primary) 62F35. (Secondary)

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http://salesiana.dossiersoluciones.com/handle/123456789/130750

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0504513
 http://arxiv.org/abs/math/0504513
 Annals of Statistics 2005, Vol. 33, No. 1, 347-380
 doi:10.1214/009053604000000940

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arXiv