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Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data
Authors:Wenqin Pan  Donglin Zeng  Xihong Lin
Institution:Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27705, U.S.A.;Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.;Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
Abstract:Summary .  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.
Keywords:Asymptotic efficiency  Conditional score method  Functional modeling  Longitudinal data  Measurement error  Transition models
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