In this paper, we propose improved statistical inference and variable selection methods for generalized linear models based on empirical likelihood approach that accommodates both the within-subject correlations and nonignorable dropouts. We first apply the generalized method of moments to estimate the parameters in the nonignorable dropout propensity based on an instrument. The inverse probability weighting is applied to obtain the bias-corrected generalized estimating equations (GEEs), and then we borrow the idea of quadratic inference function and hybrid GEE to construct the empirical likelihood procedures for longitudinal data with nonignorable dropouts, respectively. Two different classes of estimators and their confidence regions are derived. Further, the penalized EL method and algorithm for variable selection are investigated. The finite-sample performance of the proposed estimators is studied through simulation, and an application to HIV-CD4 data set is also presented.

中文翻译：

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改进了具有纵向不可忽略丢失的广义线性模型的经验似然推断和变量选择

在本文中，我们提出了基于经验似然方法的广义线性模型的改进统计推断和变量选择方法，该方法同时适应了主体内相关性和不可忽略的丢失。我们首先应用广义矩方法来估计基于工具的不可忽略的辍学倾向中的参数。应用逆概率加权获得偏差校正广义估计方程（GEE），然后我们借用二次推理函数和混合 GEE 的思想分别构建具有不可忽略丢失的纵向数据的经验似然程序。导出了两类不同的估计量及其置信区域。此外，研究了变量选择的惩罚 EL 方法和算法。