Wavelet Functional ANOVA, Bayesian False Discovery Rate, and Longitudinal Measurements of Oxygen Pressure in Rats

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dc.contributor.author Rosner, Gary L.
dc.contributor.author Vidakovic, Brani
dc.date.accessioned 2008-12-10T20:39:42Z
dc.date.available 2008-12-10T20:39:42Z
dc.date.issued 2000
dc.identifier.uri http://hdl.handle.net/1853/25943
dc.description.abstract Linear 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.iso en_US en
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries Biomedical Engineering Technical Report ; G02/2000 en
dc.subject Wavelet ANOVA en
dc.subject Shrinkage en
dc.subject Adaptivity en
dc.subject Denoising en
dc.subject Bayesian hierarchical models en
dc.title Wavelet Functional ANOVA, Bayesian False Discovery Rate, and Longitudinal Measurements of Oxygen Pressure in Rats en
dc.type Technical Report en
dc.contributor.corporatename Duke University. Institute of Statistics and Decision Sciences
dc.contributor.corporatename Duke University. Dept. of Community and Family Medicine


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