Queen's University - Utility Bar

QSpace at Queen's University >
Graduate Theses, Dissertations and Projects >
Queen's Graduate Theses and Dissertations >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/7617

This item is restricted and will be released 2017-10-28.

Title: Air Pollution and Health: Time Series Tools and Analysis
Authors: Burr, WESLEY SAMUEL

Files in This Item:

File Description SizeFormat
Burr_Wesley_S_201210_PhD.pdf2.01 MBAdobe PDFView/Open
Keywords: time series analysis
generalized additive models
Air Health Indicator
environmental health
Issue Date: 29-Oct-2012
Series/Report no.: Canadian theses
Abstract: This 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.
Description: Thesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2012-10-26 14:32:00.678
URI: http://hdl.handle.net/1974/7617
Appears in Collections:Queen's Graduate Theses and Dissertations
Department of Mathematics and Statistics Graduate Theses

Items in QSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


  DSpace Software Copyright © 2002-2008  The DSpace Foundation - TOP