Einbeck, J. and Tutz, G. (2006) 'Modelling beyond regression functions : an application of multimodal regression to speed-flow data.', Applied statistics : a journal of the Royal Statistical Society., 55 (4). pp. 461-475.
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.
|Keywords:||Conditional density, Multi-valued regression, Smoothing, Speed-flow curves.|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||http://dx.doi.org/10.1111/j.1467-9876.2006.00547.x|
|Publisher statement:||This is the accepted version of the following article: Einbeck, J. and Tutz, G. (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(4): 461-475, which has been published in final form at http://dx.doi.org/10.1111/j.1467-9876.2006.00547.x. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.|
|Date accepted:||No date available|
|Date deposited:||09 May 2016|
|Date of first online publication:||August 2006|
|Date first made open access:||No date available|
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