Estimation in semiparametric transition measurement error models for longitudinal data
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Published on: December 9, 2015
Wenqin Pan1, Donglin Zeng, Xihong Lin
1Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, USA. wendy.pan@duke.edu
Abstract:
We consider semiparametric transition measurement error models for longitudinal data, where one of the covariates is measured with error in transition models, and no distributional assumption is made for the underlying unobserved covariate. An estimating equation approach based on the pseudo conditional score method is proposed. We show the resulting estimators of the regression coefficients are consistent and asymptotically normal. We also discuss the issue of efficiency loss. Simulation studies are conducted to examine the finite-sample performance of our estimators. The longitudinal AIDS Costs and Services Utilization Survey data are analyzed for illustration.
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