Nonparametric additive regression for repeatedly measured data Academic Article

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.

authors

• Carroll, Raymond

published proceedings

• BIOMETRIKA

author list (cited authors)

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

citation count

• 20

complete list of authors

• Carroll, Raymond J||Maity, Arnab||Mammen, Enno||Yu, Kyusang

publication date

• June 2009

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Additive Model
• Generalized Least Square
• Nonparametric Regression
• Repeated Measure
• Smooth Backfitting

Digital Object Identifier (DOI)

• 10.1093/biomet/asp015

start page

• 383

end page

• 398

volume

• 96

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2Fasp015