| dc.contributor.author | Dotto, Francesco | |
| dc.contributor.author | Farcomeni, Alessio | |
| dc.contributor.author | García Escudero, Luis Ángel | |
| dc.contributor.author | Mayo Iscar, Agustín | |
| dc.date.accessioned | 2016-07-21T12:05:51Z | |
| dc.date.available | 2016-07-21T12:05:51Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | http://uvadoc.uva.es/handle/10324/18092 | |
| dc.description.abstract | new robust fuzzy linear clustering method is proposed. We estimate coe cients
of a linear regression model in each unknown cluster. Our method aims to achieve
robustness by trimming a xed proportion of observations. Assignments to clusters
are fuzzy: observations contribute to estimates in more than one single cluster. We
describe general criteria for tuning the method. The proposed method seems to be
robust with respect to di erent types of contamination. | es |
| dc.format.mimetype | application/pdf | es |
| dc.language.iso | spa | es |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Estadística | es |
| dc.title | A Fuzzy Approach to Robust Clusterwise Regression | es |
| dc.type | info:eu-repo/semantics/preprint | es |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International