tailieunhanh - Modelling the e®ects of air pollution on health using Bayesian Dynamic Generalised Linear Models

Targeting households and firms: influencing location choices. The demand side of balanced urban development involves measures to influence where households and firms choose to locate in an urban area. Although they are not yet well established, particularly in the developing countries, policies to influence location choices have led to some interesting experiments, including a “reverse” zoning scheme in the Netherlands (the “ABC” policy) and a mortgage instrument based on “location efficiency” in the United States. Targeting the general public: influencing public attitudes towards transportation. Public acceptance of policy-making on both local pollutant and greenhouse gas emissions reductions. | QB University xz of Glasgow Lee D. and Shaddick G. 2008 Modelling the effects of air pollution on health using Bayesian dynamic generalised linear models. Environmetrics 19 8 . pp. 785-804. ISSN 1180-4009 http 36768 Deposited on 07 September 2010 Enlighten - Research publications by members of the University of Glasgow http Modelling the effects of air pollution on health using Bayesian Dynamic Generalised Linear Models Duncan Lee1 and Gavin Shaddick2 November 7 2007 1 University of Glasgow and 2 University of Bath Short title - Dynamic models for air pollution and health data 0Address for correspondence Duncan Lee Department of Statistics 15 University Gardens University of Glasgow G12 8QQ E-mail duncan@ 1 Abstract The relationship between short-term exposure to air pollution and mortality or morbidity has been the subject of much recent research in which the standard method of analysis uses Poisson linear or additive models. In this paper we use a Bayesian dynamic generalised linear model DGLM to estimate this relationship which allows the standard linear or additive model to be extended in two ways i the long-term trend and temporal correlation present in the health data can be modelled by an autoregressive process rather than a smooth function of calendar time ii the e ects of air pollution are allowed to evolve over time. The e cacy of these two extensions are investigated by applying a series of dynamic and non-dynamic models to air pollution and mortality data from Greater London. A Bayesian approach is taken throughout and a Markov chain monte carlo simulation algorithm is presented for inference. An alternative likelihood based analysis is also presented in order to allow a direct comparison with the only previous analysis of air pollution and health data using a DGLM. Key words dynamic generalised linear model Bayesian analysis Markov chain monte carlo simulation air pollution