Fast methods for spatially correlated multilevel functional data

Handle URI:
http://hdl.handle.net/10754/598316
Title:
Fast methods for spatially correlated multilevel functional data
Authors:
Staicu, A.-M.; Crainiceanu, C. M.; Carroll, R. J.
Abstract:
We 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.
Citation:
Staicu 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.
Publisher:
Oxford University Press (OUP)
Journal:
Biostatistics
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
19-Jan-2010
DOI:
10.1093/biostatistics/kxp058
PubMed ID:
20089508
PubMed Central ID:
PMC2830578
Type:
Article
ISSN:
1465-4644; 1468-4357
Sponsors:
Brunel 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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorStaicu, A.-M.en
dc.contributor.authorCrainiceanu, C. M.en
dc.contributor.authorCarroll, R. J.en
dc.date.accessioned2016-02-25T13:18:35Zen
dc.date.available2016-02-25T13:18:35Zen
dc.date.issued2010-01-19en
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.en
dc.identifier.issn1465-4644en
dc.identifier.issn1468-4357en
dc.identifier.pmid20089508en
dc.identifier.doi10.1093/biostatistics/kxp058en
dc.identifier.urihttp://hdl.handle.net/10754/598316en
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.en
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.en
dc.publisherOxford University Press (OUP)en
dc.subjectColon carcinogenesisen
dc.subjectCovariogram estimationen
dc.subjectFunctional data analysisen
dc.subjectHierarchical modelingen
dc.subjectMixed modelsen
dc.subjectSpatial modelingen
dc.subject.meshModels, Statisticalen
dc.titleFast methods for spatially correlated multilevel functional dataen
dc.typeArticleen
dc.identifier.journalBiostatisticsen
dc.identifier.pmcidPMC2830578en
dc.contributor.institutionDepartment of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27695-8203, USA. staicu@stat.ncsu.eduen
kaust.grant.numberKUS-CI-016-04en

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