Nonparametric additive regression for repeatedly measured data

R. J. Carroll, A. Maity, E. Mammen, K. Yu

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)383-398
Number of pages16
Issue number2
StatePublished - May 20 2009
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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