Modelling Beyond Regression Functions: An Application of Multimodal Regression to Speed–Flow Data

Einbeck, Jochen; Tutz, Gerhard

doi:10.1111/j.1467-9876.2006.00547.x

Cited by 42 publications

(60 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Einbeck and Tutz (2006) provide an example for nonparametric estimation in the bivariate scatterplot smoothing setup relying on kernel smoothing. While modal regression is interesting from an applied perspective as an alternative to mixture models for multi-modal response distributions, inference is at the moment limited to rather simple regression specifications and therefore not yet readily combined with the full complexity of semiparametric regression.…”

Section: Discussionmentioning

confidence: 99%

Beyond mean regression

Kneib

2013

Statistical Modelling

119

View full text Add to dashboard Cite

Usual exponential family regression models focus on only one designated quantity of the response distribution, namely the mean. While this entails easy interpretation of the estimated regression effects, it may often lead to incomplete analyses when more complex relationships are indeed present and also bears the risk of false conclusions about the significance/importance of covariates. We will therefore give an overview on extended types of regression models that allows us to go beyond mean regression. More specifically, we will consider generalized additive models for location, scale and shape as well as semiparametric quantile and expectile regression. We will review the basic properties of all three approaches and compare them with respect to the flexibility in terms of the supported types of predictor specification, the availability of software and the support for different types of inferential procedures. The considered model classes are illustrated using a data set on rents for flats in the City of Munich.

show abstract

Section: Discussionmentioning

confidence: 99%

Beyond mean regression

Kneib

2013

Statistical Modelling

119

View full text Add to dashboard Cite

show abstract

“…This way, the local modes could be found by examining the response distribution separately and successively in several vertical layers. This idea is in spirit somewhat similar to having several (vertically spread) starting points for the conditional mean shift procedure for modal regression, as explained in Einbeck & Tutz (2006). Figure 1 provides some examples for modal regression in the context of the Munich rental data (which were used in the discussed paper), where, as in Figure 1 (left) of the main paper, x ='living area' is the only predictor, and y ='rent'…”

Section: (2013)mentioning

confidence: 95%

Discussion of ‘Beyond mean regression’

Einbeck

2013

Statistical Modelling

Self Cite

View full text Add to dashboard Cite

The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

show abstract

“…This need has led to the development of multi regime models and by extension to data-driven approaches. A multimodal regression to speed-flow data has been performed by Einbeck and Tutz [28]. Sun and Zhou [73] used cluster analysis in order to determine the regime boundaries for traditional speed-density models.…”

Section: Historical Backgroundmentioning

confidence: 99%

Simulation Optimization of Car-Following Models Using Flexible Techniques

Papathanasopoulou

Antoniou

2015

Computational Methods in Applied Sciences

View full text Add to dashboard Cite

Car-following behavior is a key component of microscopic traffic simulation. Numerous models based on traffic flow theory have been developed for decades in order to represent the longitudinal interactions between vehicles as realistically as possible. Nowadays, there is a shift from conventional models to data-driven approaches. Data-driven methods are more flexible and allow the incorporation of additional information to the estimation of car-following models. On the other hand, conventional car-following models are founded on traffic flow theory, thus providing better insight into traffic behavior. The integration of data-driven methods in applications of intelligent transportation systems is an attractive perspective. Towards this direction, in this research an existing data-driven approach is further validated using another training dataset. Then, the methodology is enriched and an improved methodological framework is suggested for the optimization of car-following models. Machine learning techniques, such as classification, locally weighted regression (loess) and clustering, are innovatively integrated. In this chapter, validation of the proposed methods is demonstrated on data from two sources: (i) data collected from a sequence of instrumented vehicles in Naples, Italy, and (ii) data from the NGSIM project. In addition, a conventional car-following model, the Gipps' model, is used as reference in order to monitor and evaluate the effectiveness of the proposed method. Based on the encouraging results, it is suggested that machine learning methods should be further investigated as they could ensure reliability and improvement in data driven estimation of car-following models.

show abstract

Modelling Beyond Regression Functions: An Application of Multimodal Regression to Speed–Flow Data

Cited by 42 publications

References 60 publications

Beyond mean regression

Beyond mean regression

Discussion of ‘Beyond mean regression’

Simulation Optimization of Car-Following Models Using Flexible Techniques

Contact Info

Product

Resources

About