吉林大学学报(理学版)

• 数学 • 上一篇    下一篇

协变量缺失下基于诱导光滑方法的加权分位数回归

袁晓惠1, 刘天庆2   

  1. 1. 长春工业大学 基础科学学院, 长春 130012; 2. 吉林大学 数学学院, 长春 130012
  • 收稿日期:2016-03-07 出版日期:2016-11-26 发布日期:2016-11-29
  • 通讯作者: 刘天庆 E-mail:tqliu@jlu.edu.cn

Weighted Quantile Regression Based on InducedSmoothing Method with Missing Covariates

YUAN Xiaohui1, LIU Tianqing2   

  1. 1. School of Basic Science, Changchun University of Technology, Changchun 130012, China;2. College of Mathematics, Jilin University, Changchun 130012, China
  • Received:2016-03-07 Online:2016-11-26 Published:2016-11-29
  • Contact: LIU Tianqing E-mail:tqliu@jlu.edu.cn

摘要: 在部分协变量随机缺失机制下的分位数回归模型中, 提出回归参数的诱导光滑加权估计及其渐近协方差估计, 证明了诱导光滑加权估计和经验似然加权估计有相同的渐近协方差, 且诱导光滑加权估计的渐近协方差估计也是相合的, 并给出了诱导光滑加权估计及其渐近协方差估计的高效算法. 模拟结果表明, 新方法在有限样本下表现优良.

关键词: 协变量缺失, 诱导光滑, 分位数回归, 经验似然加权估计

Abstract: We proposed an induced smoothingbased weighted estimator of regression parameter and an estimator of its asymptotic covariance in quantile regression model of partial covariates with random missing mechanism. We showed that the induced smoothingbased weighted estimator and empirical likelihoodbased weighted estimator had the same asymptotic covariance and the estimator of the asymptotic covariance of the induced smoothingbased weighted estimator was also consistent. We gave an efficient algorithm for calculating the induced smoothingbased weighted estimator of regression parameter and the estimator of its asymptotic covariance. Simulation results show that the proposed method performs well in finite samples.

Key words: quantile regression, missing covariates, empirical likelihoodbased weighted estimator, induced smoothing

中图分类号: 

  • O212.4