%0 Journal Article
%T 单指标模型的加权复合分位数回归<br>Single-Index Weighted Composite Quantile Regression
%A 杨紫微
%A 姜荣
%J Statistics and Applications
%P 766-776
%@ 2325-226X
%D 2019
%I Hans Publishing
%R 10.12677/SA.2019.85087
%X <div style=\"text-align:justify;\">
	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。<br />
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.
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%K 单指标模型，复合分位数回归，分位数回归<br>Single-Index Model
%K Composite Quantile Regression
%K Quantile Regression
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=32669