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dc.contributor.authorBurr, Wesley Samuel
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2012-10-26 14:32:00.678en
dc.date.accessioned2012-10-29T20:26:29Z
dc.date.issued2012-10-29
dc.identifier.urihttp://hdl.handle.net/1974/7617
dc.descriptionThesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2012-10-26 14:32:00.678en
dc.description.abstractThis thesis is concerned, loosely, with time series analysis. It is also, loosely, concerned with smoothers and Generalized Additive Models. And, finally, it is also concerned with the estimation of health risk due to air pollution. In the field of time series analysis, we develop two data-driven interpolation algorithms for interpolation of mixed time series data; that is, data which has a stationary or “almost” stationary background with embedded deterministic trend and sinusoidal components. These interpolators are developed to deal with the problem of estimating power spectra under the condition that some observations of the series are unavailable. We examine the structure of time-based cubic regression spline smoothers in Generalized Additive Models and demonstrate several interpretation problems with the resultant models. We propose, implement, and test a replacement smoother and show dramatic improvement. We further demonstrate a new, spectrally motivated way of examining residuals in Generalized Additive Models which drives many of the findings of this thesis. Finally, we create and analyze a large-scale Canadian air pollution and mortality database. In the course of analyzing the data we rebuild the standard risk estimation model and demonstrate several improvements. We conclude with a comparison of the original model and the updated model and show that the new model gives consistently more positive risk estimates.en_US
dc.languageenen
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectTime Series Analysisen_US
dc.subjectGeneralized Additive Modelsen_US
dc.subjectAir Health Indicatoren_US
dc.subjectStatisticsen_US
dc.subjectEpidemiologyen_US
dc.subjectEnvironmental Healthen_US
dc.titleAir Pollution and Health: Time Series Tools and Analysisen_US
dc.typeThesisen_US
dc.description.restricted-thesisShort restriction to allow the material from Chapter 5 to be published (via peer-review) in a cautious manner. The implications of the statistics in this thesis may have strong impact upon US and Canada environmental protection standards, and I wish to take care with the publication of the results. The likely length of restriction will be ~ 8-12 months only, and I will lift it as soon as the papers have passed peer review.en
dc.description.degreePh.Den
dc.contributor.supervisorThomson, David J.en
dc.contributor.departmentMathematics and Statisticsen
dc.embargo.terms1825en
dc.embargo.liftdate2017-10-28


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