Abstract

We consider a generalized partially linear model with missing outcomes

in longitudinal studies. Our proposed method, the longitudinal augmented inverse probability weighted kernel-profile estimating equations, employs kernel

estimating equations for the nonparametric part and profile estimating equations

for the parametric part. Auxiliary variables are used to model both the missingness and the conditional mean. The resulting estimators for both the parametric

and nonparametric parts are doubly robust. To further understand these estimators, we derive the semiparametric efficiency bound and the asymptotic properties

of the proposed estimators. We find that the estimator for the parametric part

attains the semiparametric efficiency bound under the multivariate normal assumption. We demonstrate the empirical performance of the proposed method

through simulation studies and an application to CD4 count data.

Information

Preprint No.SS-2024-0380
Manuscript IDSS-2024-0380
Complete AuthorsZhongzhe Ouyang, Chang Wang, Lu Wang
Corresponding AuthorsLu Wang
Emailsluwang@umich.edu

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