KAUST Grant NumberKUS-CI-016-04
Online Publication Date2010-01-19
Print Publication Date2010-04-01
Permanent link to this recordhttp://hdl.handle.net/10754/598316
MetadataShow full item record
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.
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.
SponsorsBrunel 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.
PublisherOxford University Press (OUP)
PubMed Central IDPMC2830578
CollectionsPublications Acknowledging KAUST Support
- Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis.
- Authors: Baladandayuthapani V, Mallick BK, Young Hong M, Lupton JR, Turner ND, Carroll RJ
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- Modeling functional data with spatially heterogeneous shape characteristics.
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- Issue date: 2012 Jun
- Nonparametric methods for measurements below detection limit.
- Authors: Zhang D, Fan C, Zhang J, Zhang CH
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- Interpreting statistical evidence with empirical likelihood functions.
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- Issue date: 2009 Aug