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dc.contributor.authorEttinger, Bree
dc.date.accessioned2014-03-04T18:18:46Z
dc.date.available2014-03-04T18:18:46Z
dc.date.issued2009-08
dc.identifier.otherettinger_bree_d_200908_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/ettinger_bree_d_200908_phd
dc.identifier.urihttp://hdl.handle.net/10724/25790
dc.description.abstractFor ground level ozone prediction, we consider a functional linear regression model where the explanatory variable is a real random surface and the response is a real random variable. We use bivariate splines over triangulations to represent the random surfaces. Then we use this representation to construct two solutions, a least squares estimate of the regression function based on a brute force approach, and an autoregressive estimator based on a principal component analysis. We apply these two functional linear models to ground level ozone forecasting over the United States to illustrate the predictive skills of these two methods. We also extend the brute force approach to a model where both the explanatory variable and the response are both real random surfaces.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectFunctional Linear Models
dc.subjectFunctional Data
dc.subjectRegression
dc.subjectSplines
dc.subjectPrinciple Component Analysis
dc.titleBivariate splines for ozone concentration predictions
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentMathematics
dc.description.majorMathematics
dc.description.advisorMing-Jun Lai
dc.description.committeeMing-Jun Lai
dc.description.committeeRobert Varley, Jr.
dc.description.committeeCaner Kazanci
dc.description.committeeEdward Azoff
dc.description.committeeMalcolm Adams


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