论文标题
群集数据的分位数回归中偏置调整的估计器
A bias-adjusted estimator in quantile regression for clustered data
论文作者
论文摘要
手稿讨论了如何将群集回归模型的随机效应纳入群集数据，重点是许多群集以外的设置。该论文有三个贡献：（i）记录现有方法可能导致固定效应参数的严重偏置估计值； （ii）提出了一种新的两步估计方法，其中首先通过伪可能方法（LQMM方法）计算随机效应的预测，然后用作标准分位数回归中的偏移； （iii）提出一种新型的自举采样程序，以减少两步估计器的偏差和计算置信区间。提出的估计和相关的推断是通过严格的模拟研究来评估数值的，并应用于AIDS临床试验组（ACTG）研究。
The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has three contributions: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed {by a pseudo likelihood approach (the LQMM method)} and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.
