A Fuzzy Approach to Robust Clusterwise Regression

Título

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