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dc.contributor.authorRosner, Gary L.
dc.contributor.authorVidakovic, Brani
dc.date.accessioned2008-12-10T20:39:42Z
dc.date.available2008-12-10T20:39:42Z
dc.date.issued2000
dc.identifier.urihttp://hdl.handle.net/1853/25943
dc.description.abstractLinear models can be functional in terms of independent or response variables or both. In functional ANOVA-type models often used to model longitudinal measurements and general time series, however, all components have a functional form. One of the main problems in inference using such models is the intrinsic dependence in “time” that makes pointwise inference difficult. We propose performing the inference in the wavelet domain instead of the time domain. Transformations by orthogonal wavelets preserve the structure of the linear model and, at the same time, decorrelate the data. The proposed methodology is applied to longitudinal measurements from experiments measuring oxygen pressure in tumor-bearing rats.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.relation.ispartofseriesBiomedical Engineering Technical Report ; G02/2000en
dc.subjectWavelet ANOVAen
dc.subjectShrinkageen
dc.subjectAdaptivityen
dc.subjectDenoisingen
dc.subjectBayesian hierarchical modelsen
dc.titleWavelet Functional ANOVA, Bayesian False Discovery Rate, and Longitudinal Measurements of Oxygen Pressure in Ratsen
dc.typeTechnical Reporten
dc.contributor.corporatenameDuke University. Institute of Statistics and Decision Sciences
dc.contributor.corporatenameDuke University. Dept. of Community and Family Medicine


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