Improved composite quantile regression and variable selection with nonignorable dropouts
Improved composite quantile regression and variable selection with nonignorable dropouts
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改进的复合分位数回归和变量选择，具有不可忽略的丢失
DOI：
10.1142/s2010326322500101
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Lei Wang
中科院分区：
文献类型：
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作者：
Wei Ma;Lei Wang
与non-constitutable辍学和离群值，我们提出了强大的统计推断和变量选择方法的线性分位数回归模型的基础上复合分位数回归和经验似然（EL），同时容纳受试者内的相关性和non-constitutable辍学。我们的研究有三个目的。首先，我们应用广义矩方法估计基于工具的非线性辍学倾向中的参数。然后，应用逆概率加权和核平滑方法得到平滑和偏差校正的广义估计方程。其次，我们借用二次推理函数的思想，构造了非可解释性辍学者的改进EL程序。给出了估计量的渐近性质及其置信域。第三，研究了惩罚EL方法和变量选择算法。有限样本性能的估计进行了研究，通过模拟，并应用到HIV-CD 4数据集。
With nonignorable dropouts and outliers, we propose robust statistical inference and variable selection methods for linear quantile regression models based on composite quantile regression and empirical likelihood (EL) that accommodate both the within-subject correlations and nonignorable dropouts. The purpose of our study is threefold. First, we apply the generalized method of moments to estimate the parameters in the nonignorable dropout propensity based on an instrument. Subsequently, the inverse probability weighting and kernel smoothing approaches are applied to obtain the smoothed and bias-corrected generalized estimating equations. Second, we borrow the idea of quadratic inference function to construct the improved EL procedure for nonignorable dropouts. The asymptotic properties of the proposed estimators and their confidence regions are derived. Third, 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.