Cheng Hao, Wei Ying
National Academy of Innovation Strategy, China Association for Science and Technology, Beijing, China.
School of Statistics, Renmin University of China, Beijing, China.
Comput Stat. 2018 Dec;33(4):1589-603. doi: 10.1007/s00180-018-0813-z. Epub 2018 May 15.
In many applications, some covariates could be missing for various reasons. Regression quantiles could be either biased or under-powered when ignoring the missing data. Multiple imputation and EM-based augment approach have been proposed to fully utilize the data with missing covariates for quantile regression. Both methods however are computationally expensive. We propose a fast imputation algorithm (FI) to handle the missing covariates in quantile regression, which is an extension of the fractional imputation in likelihood based regressions. FI and modified imputation algorithms (FIIPW and MIIPW) are compared to existing MI and IPW approaches in the simulation studies, and applied to part of of the National Collaborative Perinatal Project study.
在许多应用中，由于各种原因，一些协变量可能会缺失。在忽略缺失数据的情况下，回归分位数可能会有偏差或功效不足。已经提出了多重填补和基于期望最大化的扩充方法，以充分利用具有缺失协变量的数据进行分位数回归。然而，这两种方法在计算上都很昂贵。我们提出了一种快速填补算法（FI）来处理分位数回归中的缺失协变量，它是基于似然回归中的分数填补的扩展。在模拟研究中，将FI和改进的填补算法（FIIPW和MIIPW）与现有的多重填补和逆概率加权方法进行了比较，并应用于国家围产期协作项目研究的一部分。
