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dc.contributor.authorMoore, Ian
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2009-02-09 11:55:42.494en
dc.date.accessioned2014-02-04T14:58:16Z
dc.date.available2014-02-04T14:58:16Z
dc.date.issued2014-02-04
dc.identifier.urihttp://hdl.handle.net/1974/8619
dc.descriptionThesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2009-02-09 11:55:42.494en
dc.description.abstractSurgical scheduling is complicated by naturally occurring, and human-induced variability in the demand for surgical services. We used time series methods to detect, model and forecast these behaviors in surgical demand time series to help improve the scheduling of scarce surgical resources. With institutional approval, we studied 47,752 surgeries undertaken at a large academic medical center over a six-year time frame. Each daily sample in this time series represented the aggregate total hours of surgeries worked on a given day. Linear terms such as periodic cycles, trends, and serial correlations explained approximately 80 percent of the variance in the raw data. We used a moving variance filter to help explain away a large share of the heteroscedastic behavior mainly attributable to surgical activities on specific US holidays, which we defined as holiday variance. In the course of this research, we made a thoughtful attempt to understand the time series structure within our surgical demand data. We also laid a foundation, for further development, of two time series techniques, the multiwindow variance filter and cyclostatogram that can be applied not only to surgical demand time series, but also to other time series problems from other disciplines. We believe that understanding the non-stationarity, in surgical demand time series, may be an important initial step in helping health care managers save critical health care dollars.en
dc.languageenen
dc.language.isoenen
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
dc.subjectSugical Demanden
dc.subjectHoliday Varianceen
dc.subjectMultitaperen
dc.subjectVariance Filteren
dc.titleForecasting and Non-Stationarity of Surgical Demand Time Seriesen
dc.typeThesisen
dc.description.degreePh.Den
dc.contributor.supervisorStrum, Daviden
dc.contributor.supervisorThomson, David J.en
dc.contributor.departmentMathematics and Statisticsen


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