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dc.contributor.authorRueda, Cristina
dc.date.accessioned2017-03-31T09:08:29Z
dc.date.available2017-03-31T09:08:29Z
dc.date.issued2013
dc.identifier.citationJournal of Multivariate Analysis. Vol 117, pp: 88-99es
dc.identifier.urihttp://uvadoc.uva.es/handle/10324/22920
dc.description.abstractThe degrees of freedom of semiparametric additive monotone models are derived using results about projections onto sums of order cones. Two important related questions are also studied, namely, the de nition of estimators for the parameter of the error term and the formulation of speci c Akaike Information Criteria statistics. Several alternatives are proposed to solve both problems and simulation experiments are conducted to compare the behavior of the di erent candidates. A new selection criterion is proposed that combines the ability to guess the model but also the e ciency to estimate the variance parameter. Finally, the criterion is used to select the model in a regression problem from a well known data set.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleDegrees of freedom and model selection in semiparametric additive monotone regressiones
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holderElsevieres
dc.identifier.doi10.1016/j.jmva.2013.02.001es
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0047259X13000158es
dc.peerreviewedSIes
dc.description.projectMinisterio de Ciencia e Innovación grant (MTM2012-37129)es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International


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