Nonparametric additive regression for repeatedly measured data

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

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)383-398
Number of pages16
JournalBiometrika
Volume96
Issue number2
DOIs
StatePublished - May 20 2009
Externally publishedYes

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