A robust method for cluster analysis
| dc.creator | Gallegos, Maria Teresa | |
| dc.creator | Ritter, Gunter | |
| dc.date | 2005-04-25 | |
| dc.date.accessioned | 2026-07-07T08:06:51Z | |
| dc.date.available | 2026-07-07T08:06:51Z | |
| dc.description | 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. | |
| dc.description | 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) | |
| dc.identifier | https://arxiv.org/abs/math/0504513 | |
| dc.identifier | http://arxiv.org/abs/math/0504513 | |
| dc.identifier | Annals of Statistics 2005, Vol. 33, No. 1, 347-380 | |
| dc.identifier | doi:10.1214/009053604000000940 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/130750 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62H30 (Primary) 62F35. (Secondary) | |
| dc.title | A robust method for cluster analysis | |
| dc.type | text |