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Generalized partial linear models with nonignorable dropouts

Author

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  • Yujing Shao

    (Nankai University)

  • Lei Wang

    (Nankai University)

Abstract

In the presence of longitudinal data with nonignorable dropouts, we propose improved estimators for generalized partial linear models that accommodate both the within-subject correlations and nonignorable missing data. To address the identifiability problem, an instrumental covariate, which is related to the response variable but unrelated to the propensity given the response variable and other covariates, is used to construct sufficient instrumental estimating equations. Subsequently, the nonparametric function is approximated by B-spline basis functions and then we construct bias-corrected generalized estimating equations based on the inverse probability weighting. In order to incorporate the within-subject correlations under an informative working correlation structure, we borrow the idea of quadratic inference function and hybrid-GEE to construct the improved empirical likelihood procedures. Under some regularity conditions, we establish asymptotic normality of the proposed estimators for the parametric components and convergence rate of the estimators for the nonparametric functions. The finite-sample performance of the proposed estimators is studied through simulations and an application to HIV-CD4 data set is also presented.

Suggested Citation

  • Yujing Shao & Lei Wang, 2022. "Generalized partial linear models with nonignorable dropouts," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 223-252, February.
    • Handle: RePEc:spr:metrik:v:85:y:2022:i:2:d:10.1007_s00184-021-00828-z
    DOI: 10.1007/s00184-021-00828-z
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    References listed on IDEAS

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    Cited by:

    1. Jierui Du & Xia Cui, 2024. "Semiparametric estimation in generalized additive partial linear models with nonignorable nonresponse data," Statistical Papers, Springer, vol. 65(5), pages 3235-3259, July.

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