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Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data
Author(s) -
Pan Wenqin,
Zeng Donglin,
Lin Xihong
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01173.x
Subject(s) - covariate , estimator , semiparametric regression , econometrics , statistics , longitudinal data , mathematics , estimating equations , observational error , regression , semiparametric model , sample (material) , regression analysis , computer science , data mining , chemistry , chromatography
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.