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dc.contributor.authorStaicu, A.-M.
dc.contributor.authorCrainiceanu, C. M.
dc.contributor.authorCarroll, R. J.
dc.date.accessioned2016-02-25T13:18:35Z
dc.date.available2016-02-25T13:18:35Z
dc.date.issued2010-01-19
dc.identifier.citationStaicu A-M, Crainiceanu CM, Carroll RJ (2010) Fast methods for spatially correlated multilevel functional data. Biostatistics 11: 177–194. Available: http://dx.doi.org/10.1093/biostatistics/kxp058.
dc.identifier.issn1465-4644
dc.identifier.issn1468-4357
dc.identifier.pmid20089508
dc.identifier.doi10.1093/biostatistics/kxp058
dc.identifier.urihttp://hdl.handle.net/10754/598316
dc.description.abstractWe propose a new methodological framework for the analysis of hierarchical functional data when the functions at the lowest level of the hierarchy are correlated. For small data sets, our methodology leads to a computational algorithm that is orders of magnitude more efficient than its closest competitor (seconds versus hours). For large data sets, our algorithm remains fast and has no current competitors. Thus, in contrast to published methods, we can now conduct routine simulations, leave-one-out analyses, and nonparametric bootstrap sampling. Our methods are inspired by and applied to data obtained from a state-of-the-art colon carcinogenesis scientific experiment. However, our models are general and will be relevant to many new data sets where the object of inference are functions or images that remain dependent even after conditioning on the subject on which they are measured. Supplementary materials are available at Biostatistics online.
dc.description.sponsorshipBrunel Fellowship from the University of Bristol to A.-M.S.; National Institute of Neurological Disorders and Stroke (R01NS060910) to C.M.C.; National Cancer Institute (CA57030) and King Abdullah University of Science and Technology (KUS-CI-016-04) to R.J.C.
dc.publisherOxford University Press (OUP)
dc.subjectColon carcinogenesis
dc.subjectCovariogram estimation
dc.subjectFunctional data analysis
dc.subjectHierarchical modeling
dc.subjectMixed models
dc.subjectSpatial modeling
dc.subject.meshModels, Statistical
dc.titleFast methods for spatially correlated multilevel functional data
dc.typeArticle
dc.identifier.journalBiostatistics
dc.identifier.pmcidPMC2830578
dc.contributor.institutionDepartment of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695-8203, USA. staicu@stat.ncsu.edu
kaust.grant.numberKUS-CI-016-04
dc.date.published-online2010-01-19
dc.date.published-print2010-04-01


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