Publication

Modelling beyond regression functions: an application of multimodal regression to speed–flow data

Einbeck, Jochen
; 
Tutz, Gerhard
Identifiers
http://hdl.handle.net/10379/9101
 https://doi.org/10.13025/24433
Publication Date
2006-08-01
Keywords
conditional density, multivalued regression, smoothing, speed-flow curves, driven bandwidth selection, nonparametric-estimation, conditional density, principal curves, mean shift, estimators, tests
Type
Article
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Citation
Einbeck, 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
Abstract
For 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.
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Publisher
Wiley-Blackwell
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Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Externally hosted open access publications with University of Galway authors (1)