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高校应用数学学报
Applied Mathematics A Journal of Chinese Universities  2015, Vol. 30 Issue (1): 71-83    DOI:
    
Composite quantile regression estimation in non-parametric regression model under left-truncated data
WANG Jiang-feng1, TIAN Xiao-min2, ZHANG Hui-zeng2, WEN Li-min3
1. School of Statis. Math., Zhejiang Gongshang Univ., Hangzhou 310018, China
2. Dept. of Math., Hangzhou Normal Univ., Hangzhou 310036, China
3. School of Math. Sci., Jiangxi Normal Univ., Nanchang 330022, China
    School of Inform. Tech., Jiangxi Normal Univ., Nanchang 330013, China
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Abstract  In this paper, a local linear composite quantile regression estimator of regression function is constructed in the regression model with heteroscedastic error under left-truncated data. The asymptotic normality of the proposed estimator is also established. The estimator is much more efficient than the local linear regression estimator for commonly-used non-normal error distributions via simulations.

Key words： left-truncated data      non-parametric regression      composite quantile regression      asymptotic normality     
Received: 24 July 2014      Published: 06 June 2018
CLC:  O212  
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WANG Jiang-feng, TIAN Xiao-min, ZHANG Hui-zeng, WEN Li-min. Composite quantile regression estimation in non-parametric regression model under left-truncated data. Applied Mathematics A Journal of Chinese Universities, 2015, 30(1): 71-83.

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http://www.zjujournals.com/amjcua/     OR     http://www.zjujournals.com/amjcua/Y2015/V30/I1/71


左截断数据下非参数回归模型的复合分位数回归估计

利用局部多项式方法研究了误差具有异方差结构的非参数回归模型, 在左截断数据下构造了回归函数的复合分位数回归估计, 并得到了该估计的渐近正态性结果, 最后通过模拟, 在服从一些非正态分布的误差下, 得到该估计比局部线性估计更有效.

关键词： 左截断数据,  非参数回归,  复合分位数回归,  渐近正态性 
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