Corrected-loss estimation for quantile regression with covariate measurement errors.
Corrected-loss estimation for quantile regression with covariate measurement errors.
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DOI：
10.1093/biomet/ass005
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发表时间：
2012-06
期刊：
影响因子：
2.7
通讯作者：
H. Wang;L. Stefanski;Zhongyi Zhu
中科院分区：
文献类型：
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作者：
H. Wang;L. Stefanski;Zhongyi Zhu
我们研究了有误差测量协变量时的分位数回归估计。现有方法需要严格的假设，如回归变量和测量误差变量的球对称联合分布，或所有分位数函数的线性，这限制了模型的灵活性和复杂的计算。在本文中，我们开发了一种新的基于校正分数的估计方法来解释分位数回归中的一类协变量测量误差。该方法易于实现。它的有效性只需要特定分位数函数的线性，并且不需要对回归误差分布进行参数假设。有限样本结果表明，在考虑的各种模型中，所提出的估计方法比现有的估计方法更有效。
We study estimation in quantile regression when covariates are measured with errors. Existing methods require stringent assumptions, such as spherically symmetric joint distribution of the regression and measurement error variables, or linearity of all quantile functions, which restrict model flexibility and complicate computation. In this paper, we develop a new estimation approach based on corrected scores to account for a class of covariate measurement errors in quantile regression. The proposed method is simple to implement. Its validity requires only linearity of the particular quantile function of interest, and it requires no parametric assumptions on the regression error distributions. Finite-sample results demonstrate that the proposed estimators are more efficient than the existing methods in various models considered.