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dc.contributor.authorSerban, Nicoleta
dc.contributor.authorStaicu, Ana-Maria
dc.contributor.authorCarroll, Raymond J.
dc.date.accessioned2016-02-25T13:43:27Z
dc.date.available2016-02-25T13:43:27Z
dc.date.issued2013-10-16
dc.identifier.citationSerban N, Staicu A-M, Carroll RJ (2013) Multilevel Cross-Dependent Binary Longitudinal Data. Biom 69: 903–913. Available: http://dx.doi.org/10.1111/biom.12083.
dc.identifier.issn0006-341X
dc.identifier.pmid24131242
dc.identifier.doi10.1111/biom.12083
dc.identifier.urihttp://hdl.handle.net/10754/598905
dc.description.abstractWe provide insights into new methodology for the analysis of multilevel binary data observed longitudinally, when the repeated longitudinal measurements are correlated. The proposed model is logistic functional regression conditioned on three latent processes describing the within- and between-variability, and describing the cross-dependence of the repeated longitudinal measurements. We estimate the model components without employing mixed-effects modeling but assuming an approximation to the logistic link function. The primary objectives of this article are to highlight the challenges in the estimation of the model components, to compare two approximations to the logistic regression function, linear and exponential, and to discuss their advantages and limitations. The linear approximation is computationally efficient whereas the exponential approximation applies for rare events functional data. Our methods are inspired by and applied to a scientific experiment on spectral backscatter from long range infrared light detection and ranging (LIDAR) data. The models are general and relevant to many new binary functional data sets, with or without dependence between repeated functional measurements.
dc.description.sponsorshipSerban's research was supported by the National Science Foundation Grant CMMI-0954283. Staicu's research was supported by U.S. National Science Foundation grant number DMS-1007466. Carroll's research was supported by the National Cancer Institute Grant R37-CA057030 and in part supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank to the referees and associate editor for helpful comments.
dc.publisherWiley
dc.subjectFunctional Data Analysis
dc.subjectMixed Models
dc.subjectHierarchical Modeling
dc.subjectBinary Longitudinal Data
dc.subjectCovariogram Estimation
dc.subjectCross-dependent Functional Data
dc.subjectMultilevel Functional Data
dc.subjectPrincipal Component Estimation
dc.subject.meshData Interpretation, Statistical
dc.subject.meshModels, Statistical
dc.subject.meshLogistic Models
dc.subject.meshLongitudinal Studies
dc.titleMultilevel Cross-Dependent Binary Longitudinal Data
dc.typeArticle
dc.identifier.journalBiometrics
dc.identifier.pmcidPMC3865135
dc.contributor.institutionH. Milton Stewart School of Industrial Systems and Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, Georgia, 30318, U.S.A.
kaust.grant.numberKUS-CI-016-04
dc.date.published-online2013-10-16
dc.date.published-print2013-12


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