Dimensionality reduction for samples of bivariate density level sets: an application to electoral results

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/106516

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.contributor	Universitat Politècnica de Catalunya. ADBD - Anàlisi de Dades Complexes per a les Decisions Empresarials
dc.contributor.author	Delicado Useros, Pedro Francisco
dc.date	2017
dc.identifier.citation	Delicado, P. Dimensionality reduction for samples of bivariate density level sets: an application to electoral results. A: International Workshop on Functional and Operatorial Statistics. "Recent Advances in Functional Data Analysis and Related Topics". Santander: Springer, 2017, p. 71-76.
dc.identifier.citation	978-3-7908-2736-1
dc.identifier.citation	10.1007/978-3-7908-2736-1_11
dc.identifier.uri	http://hdl.handle.net/2117/106516
dc.language.iso	eng
dc.publisher	Springer
dc.relation	https://link.springer.com/chapter/10.1007/978-3-7908-2736-1_11
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa
dc.subject	Classificació AMS::90 Operations research, mathematical programming
dc.title	Dimensionality reduction for samples of bivariate density level sets: an application to electoral results
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	The final publication is available at link.springer.com
dc.description.abstract	A bivariate densities can be represented as a density level set containing a fixed amount of probability (0.75, for instance). Then a functional dataset where the observations are bivariate density functions can be analyzed as if the functional data are density level sets.We compute distances between sets and perform standard Multidimensional Scaling. This methodology is applied to analyze electoral results.
dc.description.abstract	Peer Reviewed

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