摘要
在进行回归分析时,对误差项离散程度的度量是一个重要话题.文章利用最小化复合分位损失的方法,对误差项的尺度参数进行估计,并证明估计量的大样本性质.进一步的研究表明：通过选取合适的分位数,能得到尺度参数的最优估计,并以此进行异质性检验.模拟结果表明,在重尾条件下所提出的方法有更高的精度.实际数据应用体现了该方法的良好性能.
When it comes to regression analysis, the measurement of residuals＇ scale is an important issue. In this paper, we propose an estimator of the scale-parameter of residuals by minimizing the composite quantile loss function and prove its large sample properties. Further research shows that by selecting appropriate quantiles,we could optimize the proposed estimator. The heteroscedasticity is tested based on the resulting estimators results. Simulation shows that the method is more effective in estimating the scale-parameter and testing heteroscedasticity compared with other methods under the setting of heavy-tailed distributions. Applications of this method to real data show its good performance.
作者
苏鹏
田茂再
SU Peng;TIAN Maozai(Center for Applied Statistics,School of Statistics,Renmin University of China,Beijing 100872;School of Statistics,Lanzhou University of Finance and Economics,Lanzhou 730020;School of Statistics and Information,Xinjiang University of Finance and Economics,Urumqi 830012)
出处
《系统科学与数学》
CSCD
北大核心
2018年第9期1055-1066,共12页
Journal of Systems Science and Mathematical Sciences
基金
中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目成果(18XNL012)资助课题
关键词
复合分位损失
尺度参数估计
异质性检验
Composite quantile loss function
estimation of scale-parameter
detec-tion of heteroscedasticity
