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dc.contributor.authorLoader, Catherineen_US
dc.date.accessioned2009-01-29T14:54:12Z-
dc.date.available2009-01-29T14:54:12Z-
dc.date.issued2004en_US
dc.identifier.urihttp://hdl.handle.net/10419/22186-
dc.description.abstractSmoothing methods attempt to find functional relationships between different measurements. As in the standard regression setting, the data is assumed to consist of measurements of a response variable, and one or more predictor variables. Standard regression techniques (Chapter ??) specify a functional form (such as a straight line) to describe the relation between the predictor and response variables. Smoothing methods take a more flexible approach, allowing the data points themselves to determine the form of the fitted curve. This article begins by describing several different approaches to smoothing, including kernel methods, local regression, spline methods and orthogonal series. A general theory of linear smoothing is presented, which allows us to develop methods for statistical inference, model diagnostics and choice of smoothing parameters. The theory is then extended to more general settings, including multivariate smoothing and likelihood models.en_US
dc.language.isoengen_US
dc.publisheren_US
dc.relation.ispartofseriesPapers / Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE) 2004,12en_US
dc.subject.ddc330en_US
dc.titleSmoothing: Local Regression Techniquesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn495305308en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:caseps:200412-
Appears in Collections:Papers, CASE - Center for Applied Statistics and Economics, HU Berlin

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