Zhang, Yong (2000) Use of spatial models and the MCMC method for investigating the relationship between road traffic pollution and asthma amongst children. PhD thesis, University of Greenwich.

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Abstract

This thesis uses two datasets: NCDS (National Child Development Study) and Bartholomew's Digital road map to investigate the relationship between road traffic pollution and asthma amongst children. A pollution exposure model is developed to provide an indicator of road traffic pollution. Also, a spatially driven logistic regression model of the risk of asthma occurrence is developed. The relationship between asthma and pollution is tested using this model. The power of the test has been studied.

Because of the uncertainty of exact spatial location of subjects, given a post-code, we have considered error-in-variable model, otherwise known as measurement error model. A general foundation is presented. Inference is attempted in three approaches. Compared with models without measurement error, no improvement on log-likelihood is made. We suggest the error can be omitted.

We also take a Bayesian approach to analyse the relationship. A discretized MCMC (Markov Chain Monte Carlo) is developed so that it can be used to estimate parameters and to do inference on a very complex posterior density function. It extends the simulated tempering method to 'multi-dimension temperature' situation. We use this method to implement MCMC on our models. The improvement in speed is remarkable.

A significant effect of road traffic pollution on asthma is not found. But the methodology (spatially driven logistic regression and discretized MCMC) can be applied on other data.

Item Type:	Thesis (PhD)
Additional Information:	uk.bl.ethos.550053
Uncontrolled Keywords:	road traffic pollution, childhood asthma, Bayesian probability, statistics, Markov chain Monte Carlo, MCMC
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics T Technology > T Technology (General)
Pre-2014 Departments:	School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Statistics & Operational Research Group
Last Modified:	24 Aug 2018 17:17
URI:	http://gala.gre.ac.uk/id/eprint/8247

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