Wei Ying, Ma Yanyuan, Carroll Raymond J
Department of Biostatistics, Columbia University, 722 West 168th St., New York, New York 10032, U.S.A.
Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.A.
Biometrika. 2012;99(2):423-438. doi: 10.1093/biomet/ass007.
We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root- consistent and asymptotically normal. To protect against possible model misspecification, we further propose a shrinkage estimator, which automatically adjusts for possible bias. The finite sample performance of our estimator is investigated in a simulation study. Finally, we apply our methodology to part of the Eating at American's Table Study data, investigating the association between two measures of dietary intake.
当一些协变量随机缺失时，我们提出了一种用于分位数回归模型参数估计的多重填补估计器。估计过程充分利用整个数据集以提高效率，并且所得的系数估计器是根一致且渐近正态的。为防止可能的模型误设，我们进一步提出了一种收缩估计器，它会自动调整可能的偏差。在模拟研究中考察了我们估计器的有限样本性能。最后，我们将我们的方法应用于“在美国人餐桌上用餐”研究数据的一部分，研究两种饮食摄入量测量指标之间的关联。
