Bootstrap model averaging in time series studies of particulate matter air pollution and mortality

dc.contributor.authorMartin, Michael
dc.contributor.authorRoberts, Steven
dc.date.accessioned2015-12-07T22:39:15Z
dc.date.issued2006
dc.date.updated2015-12-07T10:47:04Z
dc.description.abstractThe consensus from time series studies that have investigated the mortality effects of participate matter air pollution (PM) is that increases in PM are associated with increases in daily mortality. However, recently concerns have been raised that the observed positive association between PM and mortality may be an artefact of model selection due to multiple hypothesis testing. This problem arises when a number of models are investigated, but only the "best" model is reported and all subsequent inference is based on this model, ignoring the model selection process. In this paper, we introduce the use of the bootstrap as a means of addressing the problems of model selection in PM mortality time series studies. Using the bootstrap to perform inference about the effect of PM on mortality is a process based on a set of models rather than on a single model. It is shown that using the bootstrap to overcome the problems of model selection is competitive with the existing methodology of Bayesian model averaging.
dc.identifier.issn1559-0631
dc.identifier.urihttp://hdl.handle.net/1885/23781
dc.publisherNature Publishing Group
dc.sourceJournal of Exposure Science and Environmental Epidemiology
dc.subjectKeywords: air pollution; article; averaging; Bayes theorem; methodology; model; mortality; particulate matter; Air Pollutants; Confounding Factors (Epidemiology); Humans; Models, Theoretical; Mortality; Particle Size Air pollution; Bayesian model averaging; Model selection; Mortality; PM; Time series
dc.titleBootstrap model averaging in time series studies of particulate matter air pollution and mortality
dc.typeJournal article
local.bibliographicCitation.lastpage250
local.bibliographicCitation.startpage242
local.contributor.affiliationMartin, Michael, College of Business and Economics, ANU
local.contributor.affiliationRoberts, Steven, College of Business and Economics, ANU
local.contributor.authoremailu8517524@anu.edu.au
local.contributor.authoruidMartin, Michael, u8517524
local.contributor.authoruidRoberts, Steven, u3031871
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor010401 - Applied Statistics
local.identifier.ariespublicationu8902633xPUB28
local.identifier.citationvolume16
local.identifier.doi10.1111/j.1468-2230.2006.00583_1.x
local.identifier.scopusID2-s2.0-33646689102
local.identifier.uidSubmittedByu8902633
local.type.statusPublished Version

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