Wei Ying, Carroll Raymond J
Assistant Professor, Department of Biostatistics, Columbia University, 722 West 168th St., New York, NY 10032.
J Am Stat Assoc. 2009 Sep 1;104(487):1129-1143. doi: 10.1198/jasa.2009.tm08420.
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure.
当协变量存在测量误差时，回归分位数可能会有很大偏差。在本文中，我们提出了一种新方法，该方法在存在协变量测量误差的情况下能产生一致的线性分位数估计。该方法通过构建对所有分位数水平同时成立的联合估计方程来校正测量误差引起的偏差。提供了一种迭代的期望最大化（EM）型估计算法来求解此类联合估计方程。在模拟研究中考察了所提方法的有限样本性能，并与标准回归校准方法进行了比较。最后，我们将我们的方法应用于国家围产期协作项目生长数据的一部分，这是一项具有特殊测量误差结构的纵向研究。
