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dc.contributor.authorEinbeck, Jochen
dc.contributor.authorTutz, Gerhard
dc.date.accessioned2018-08-24T08:24:41Z
dc.date.available2018-08-24T08:24:41Z
dc.date.issued2006-08-01
dc.identifier.citationEinbeck, Jochen; Tutz, Gerhard (2006). Modelling beyond regression functions: an application of multimodal regression to speed–flow data. Journal of the Royal Statistical Society: Series C (Applied Statistics) 55 , 461-475
dc.identifier.issn0035-9254,1467-9876
dc.identifier.urihttp://hdl.handle.net/10379/9101
dc.description.abstractFor speed-flow data, which are intensively discussed in transportation science, common nonparametric regression models of the type y = m(x) + noise turn out to be inadequate since simple functional models cannot capture the essential relationship between the predictor and response. Instead a more general setting is required, allowing for multifunctions rather than functions. The tool proposed is conditional modes estimation which, in the form of local modes, yields several branches that correspond to the local modes. A simple algorithm for computing the branches is derived. This is based on a conditional mean shift algorithm and is shown to work well in the application that is considered.
dc.publisherWiley-Blackwell
dc.relation.ispartofJournal of the Royal Statistical Society: Series C (Applied Statistics)
dc.subjectconditional density
dc.subjectmultivalued regression
dc.subjectsmoothing
dc.subjectspeed-flow curves
dc.subjectdriven bandwidth selection
dc.subjectnonparametric-estimation
dc.subjectconditional density
dc.subjectprincipal curves
dc.subjectmean shift
dc.subjectestimators
dc.subjecttests
dc.titleModelling beyond regression functions: an application of multimodal regression to speed–flow data
dc.typeArticle
dc.identifier.doi10.1111/j.1467-9876.2006.00547.x
dc.local.publishedsourcehttp://www.stat.uni-muenchen.de/sfb386/papers/dsp/paper395.pdf
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