Handle URI:
http://hdl.handle.net/10754/598905
Title:
Multilevel Cross-Dependent Binary Longitudinal Data
Authors:
Serban, Nicoleta; Staicu, Ana-Maria; Carroll, Raymond J.
Abstract:
We 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.
Citation:
Serban 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.
Publisher:
Wiley-Blackwell
Journal:
Biometrics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
16-Oct-2013
DOI:
10.1111/biom.12083
PubMed ID:
24131242
PubMed Central ID:
PMC3865135
Type:
Article
ISSN:
0006-341X
Sponsors:
Serban'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.
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Full metadata record

DC FieldValue Language
dc.contributor.authorSerban, Nicoletaen
dc.contributor.authorStaicu, Ana-Mariaen
dc.contributor.authorCarroll, Raymond J.en
dc.date.accessioned2016-02-25T13:43:27Zen
dc.date.available2016-02-25T13:43:27Zen
dc.date.issued2013-10-16en
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.en
dc.identifier.issn0006-341Xen
dc.identifier.pmid24131242en
dc.identifier.doi10.1111/biom.12083en
dc.identifier.urihttp://hdl.handle.net/10754/598905en
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.en
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.en
dc.publisherWiley-Blackwellen
dc.subjectFunctional Data Analysisen
dc.subjectMixed Modelsen
dc.subjectHierarchical Modelingen
dc.subjectBinary Longitudinal Dataen
dc.subjectCovariogram Estimationen
dc.subjectCross-dependent Functional Dataen
dc.subjectMultilevel Functional Dataen
dc.subjectPrincipal Component Estimationen
dc.subject.meshData Interpretation, Statisticalen
dc.subject.meshModels, Statisticalen
dc.subject.meshLogistic Modelsen
dc.subject.meshLongitudinal Studiesen
dc.titleMultilevel Cross-Dependent Binary Longitudinal Dataen
dc.typeArticleen
dc.identifier.journalBiometricsen
dc.identifier.pmcidPMC3865135en
dc.contributor.institutionH. Milton Stewart School of Industrial Systems and Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, Georgia, 30318, U.S.A.en
kaust.grant.numberKUS-CI-016-04en
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