Document Detail

Efficient Semiparametric Marginal Estimation for the Partially Linear Additive Model for Longitudinal/Clustered Data.
MedLine Citation:
PMID:  20161464     Owner:  NLM     Status:  Publisher    
We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As part of this work, we first describe the behavior of nonparametric estimators for additive models with repeated measures when the underlying model is not additive. These results are critical when one considers variants of the basic additive model. We apply them to the partially linear additive repeated-measures model, deriving an explicit consistent estimator of the parametric component; if the errors are in addition Gaussian, the estimator is semiparametric efficient. We also apply our basic methods to a unique testing problem that arises in genetic epidemiology; in combination with a projection argument we develop an efficient and easily computed testing scheme. Simulations and an empirical example from nutritional epidemiology illustrate our methods.
Raymond Carroll; Arnab Maity; Enno Mammen; Kyusang Yu
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Publication Detail:
Journal Detail:
Title:  Statistics in biosciences     Volume:  1     ISSN:  1867-1772     ISO Abbreviation:  -     Publication Date:  2009 May 
Date Detail:
Created Date:  2010-7-13     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101498115     Medline TA:  Stat Biosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  10-31     Citation Subset:  -    
Department of Statistics, 3143 TAMU, Texas A&M University, College Station, Texas 77843, USA, , Telephone 979 845 3141, Fax 979 845 3144.
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Grant Support
R37 CA057030-21//NCI NIH HHS

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