Type
ArticleKAUST Grant Number
KUS-CI-016-04Date
2010-01-19Online Publication Date
2010-01-19Print Publication Date
2010-04-01Permanent link to this record
http://hdl.handle.net/10754/598316
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Show full item recordAbstract
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.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.Publisher
Oxford University Press (OUP)Journal
BiostatisticsPubMed ID
20089508PubMed Central ID
PMC2830578ae974a485f413a2113503eed53cd6c53
10.1093/biostatistics/kxp058
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