Let there be given a contaminated list of n ℝd-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.
Publié le : 2005-02-14
Classification:
Cluster analysis,
multivariate data,
outliers,
robustness,
breakdown point,
determinant criterion,
minimal distance partition,
62H30,
62F35
@article{1112967709,
author = {Gallegos, Mar\'\i a Teresa and Ritter, Gunter},
title = {A robust method for cluster analysis},
journal = {Ann. Statist.},
volume = {33},
number = {1},
year = {2005},
pages = { 347-380},
language = {en},
url = {http://dml.mathdoc.fr/item/1112967709}
}
Gallegos, María Teresa; Ritter, Gunter. A robust method for cluster analysis. Ann. Statist., Tome 33 (2005) no. 1, pp. 347-380. http://gdmltest.u-ga.fr/item/1112967709/