Modelling beyond regression functions: an application of multimodal regression to speed–flow data
By Jochen Einbeck, Gerhard E. Tutz. 2006
71 citations recorded by the source corpus.
Summary. 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.
Subfields: Artificial Intelligence, Building and Construction, Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/dac6a5d0fbf4ff52d07c8d70864b3e488ddcb2ef
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In Artificial Intelligence
In Building and Construction
In Civil and Structural Engineering
Citations in the corpus
- Beyond mean regression (2013, background): “Einbeck and Tutz (2006) provide an example for nonparametric estimation in the bivariate scatterplot smoothing setup relying on kernel smoothing.”
- Three new models for the flow–density relationship: derivation and testing for freeway and urban data (2012, unknown): “2004 , Einbeck and Tutz 2006 , Jiang and Huang 2009 ).”