Noncrossing quantile regression curve estimation
Noncrossing quantile regression curve estimation
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DOI：
10.1093/biomet/asq048
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发表时间：
2010-12-01
期刊：
影响因子：
2.7
通讯作者：
Wang, Huixia
中科院分区：
文献类型：
--
作者：
Bondell, Howard D.;Reich, Brian J.;Wang, Huixia
由于分位数回归曲线是单独估计的，因此分位数曲线可能会交叉，导致响应的分布无效。提出了一种简单的分位数回归的约束版本，以避免线性和非参数分位数曲线的交叉问题。对热带气旋强度资料的模拟研究和再分析表明了该方法的实用性。在标准条件下，估计量的渐近性质等价于典型的方法，如果没有交叉，则估计量退化为经典的估计量。通过在分位数水平上增加平滑和稳定性，约束估计器的性能得到了显着改善。
Since quantile regression curves are estimated individually, the quantile curves can cross, leading to an invalid distribution for the response. A simple constrained version of quantile regression is proposed to avoid the crossing problem for both linear and nonparametric quantile curves. A simulation study and a reanalysis of tropical cyclone intensity data shows the usefulness of the procedure. Asymptotic properties of the estimator are equivalent to the typical approach under standard conditions, and the proposed estimator reduces to the classical one if there is no crossing. The performance of the constrained estimator has shown significant improvement by adding smoothing and stability across the quantile levels.