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Including driver characteristics in a model of relative collision safety
Linköpings universitet, Matematisk statistik.ORCID iD: 0000-0002-9164-9221
2000 (English)Report (Other academic)
Resource type
Text
Abstract [en]

When the relative collision safety between different car makes has been estimated, it has been shown that a person's age and sex influence the injury risk in accidents that are otherwise similar. In an earlier work, relative collisionsafety in cars are studied. That model is now expanded by introducing parameters related to the driver's age and sex. Different models are compared and the "best" model is chosen by a likelihood-ratio-test. The estimated relative risks compensated for the driver's age and sex are compared to the relative risks without such information. The uncertainties of the different estimates are studied by a bootstrap analysis.

Place, publisher, year, edition, pages
2000. , 18 p.
Series
LiTH-MAT-R, ISSN 0348-2960 ; 19
Keyword [en]
Driver, Age, Man, Woman, Properties, Mathematical model, Accident, Risk
National Category
Probability Theory and Statistics
Research subject
80 Road: Traffic safety and accidents, 812 Road: Collation of accident statistics; 80 Road: Traffic safety and accidents, 85 Road: Personal injuries
Identifiers
URN: urn:nbn:se:vti:diva-8303OAI: oai:DiVA.org:vti-8303DiVA: diva2:900143
Available from: 2013-01-07 Created: 2016-02-03 Last updated: 2016-02-22Bibliographically approved
In thesis
1. Computer based statistical treatment in models with incidental parameters: inspired by car crash data
Open this publication in new window or tab >>Computer based statistical treatment in models with incidental parameters: inspired by car crash data
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Bootstrap and Markov chain Monte Carlo methods have received much attention in recent years. We study computer intensive methods that can be used in complex situations where it is not possible to express the likelihood estimates or the posterior analytically. The work is inspired by a set of car crash data from real traffic.

We formulate and develop a model for car crash data that aims to estimate and compare the relative collision safety among different car models. This model works sufficiently well, although complications arise due to a growing vector of incidental parameters. The bootstrap is shown to be a useful tool for studying uncertainties of the estimates of the structural parameters. This model is further extended to include driver characteristics. In a Poisson model with similar, but simpler structure, estimates of the structural parameter in the presence of incidental parameters are studied. The profile likelihood, bootstrap and the delta method are compared for deterministic and random incidental parameters. The same asymptotic properties, up to first order, are seen for deterministic as well as random incidental parameters.

The search for suitable methods that work in complex model structures leads us to consider Markov chain Monte Carlo (MCMC) methods. In the area of MCMC, we consider particularly the question of how and when to claim convergence of the MCMC run in situations where it is only possible to analyse the output values of the run and also how to compare different MCMC modellings. In Metropolis-Hastings algorithm, different proposal functions lead to different realisations. We develop a new convergence diagnostic, based on the Kullback-Leibler distance, which is shown to be particularly useful when comparing different runs. Comparisons with established methods turn out favourably for the KL.

In both models, a Bayesian analysis is made where the posterior distribution is obtained by MCMC methods. The credible intervals are compared to the corresponding confidence intervals from the bootstrap analysis and are shown to give the same qualitative conclusions.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 34 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 814
Keyword
Mathematical model, Accident, Statistics, Analysis, Driver, Characteristics
National Category
Mathematics
Research subject
80 Road: Traffic safety and accidents, 812 Road: Collation of accident statistics
Identifiers
urn:nbn:se:vti:diva-7106 (URN)1377 (Local ID)91-7373-625-2 (ISBN)1377 (Archive number)1377 (OAI)
Public defence
2003-05-09, Sal Visionen, Hus B, Linköpings Universitet, 13:15 (Swedish)
Opponent
Available from: 2009-10-07 Created: 2014-09-16 Last updated: 2016-02-22Bibliographically approved

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
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Output format
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