Nonparametric additive regression for repeatedly measured data

Handle URI:
http://hdl.handle.net/10754/598998
Title:
Nonparametric additive regression for repeatedly measured data
Authors:
Carroll, R. J.; Maity, A.; Mammen, E.; Yu, K.
Abstract:
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Citation:
Carroll RJ, Maity A, Mammen E, Yu K (2009) Nonparametric additive regression for repeatedly measured data. Biometrika 96: 383–398. Available: http://dx.doi.org/10.1093/biomet/asp015.
Publisher:
Oxford University Press (OUP)
Journal:
Biometrika
Issue Date:
20-May-2009
DOI:
10.1093/biomet/asp015
Type:
Article
ISSN:
0006-3444; 1464-3510
Sponsors:
The authors are grateful to the editor, associate editor and two referees for their invaluable commentsand suggestions. Yu and Mammen’s research was supported by the Deutsche Forschungsgemeinschaft.Carroll and Maity’s research was supported by grants from the National CancerInstitute. Part of Carroll’s work was supported by an award made by the King Abdullah Universityof Science and Technology.
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Full metadata record

DC FieldValue Language
dc.contributor.authorCarroll, R. J.en
dc.contributor.authorMaity, A.en
dc.contributor.authorMammen, E.en
dc.contributor.authorYu, K.en
dc.date.accessioned2016-02-25T13:50:52Zen
dc.date.available2016-02-25T13:50:52Zen
dc.date.issued2009-05-20en
dc.identifier.citationCarroll RJ, Maity A, Mammen E, Yu K (2009) Nonparametric additive regression for repeatedly measured data. Biometrika 96: 383–398. Available: http://dx.doi.org/10.1093/biomet/asp015.en
dc.identifier.issn0006-3444en
dc.identifier.issn1464-3510en
dc.identifier.doi10.1093/biomet/asp015en
dc.identifier.urihttp://hdl.handle.net/10754/598998en
dc.description.abstractWe develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.en
dc.description.sponsorshipThe authors are grateful to the editor, associate editor and two referees for their invaluable commentsand suggestions. Yu and Mammen’s research was supported by the Deutsche Forschungsgemeinschaft.Carroll and Maity’s research was supported by grants from the National CancerInstitute. Part of Carroll’s work was supported by an award made by the King Abdullah Universityof Science and Technology.en
dc.publisherOxford University Press (OUP)en
dc.subjectAdditive modelen
dc.subjectGeneralized least squareen
dc.subjectNonparametric regressionen
dc.subjectRepeated measureen
dc.subjectSmooth backfittingen
dc.titleNonparametric additive regression for repeatedly measured dataen
dc.typeArticleen
dc.identifier.journalBiometrikaen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionHarvard School of Public Health, Boston, United Statesen
dc.contributor.institutionUniversitat Mannheim, Mannheim, Germanyen
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