单指标模型的加权复合分位数回归
Single-Index Weighted Composite Quantile Regression
摘要: 加权复合分位数回归(WCQR)是在分位数回归基础上发展起来的一种稳健估计，并且其效率与半参数极大似然估计几乎相同，因而越来越受到人们的关注。近年来，WCQR方法已经被推广到了单指标模型中，但是目前单指标模型的WCQR方法都涉及到算法的迭代问题，其严重影响运算速度。为解决以上问题，本文提出一种非迭代的估计算法，并给出估计量的渐近分布。最后通过模拟和实证研究验证了本文所提出方法的有效性。
Abstract: Weighted composite quantile regression (WCQR), a robust estimation based on quantile regression, is becoming increasingly popular due to the nearly same efficiency as the semi-parametric maximum likelihood estimator. Recently, WCQR method has been promoted extensively to sin-gle-index model. However, the recent WCQR methods for single-index model are necessarily itera-tive, which seriously affects the computing speed. We propose a non-iterative estimation algorithm, and derive the asymptotic distribution of the proposed estimator. The simulation and empirical studies are conducted to illustrate the finite sample performance of the proposed methods.
文章引用：杨紫微, 姜荣. 单指标模型的加权复合分位数回归[J]. 统计学与应用, 2019, 8(5): 766-776. https://doi.org/10.12677/SA.2019.85087

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