因变量缺失下部分线性变系数模型的稳健估计  

Robust Estimators in Semi-parametric Partially Linear Varying-coefficient Models With Missing Response

作　　者：李小亮 陈艳[1] LI Xiao-liang;CHEN Yan(School of Statistics and Management,Shanghai University of Finance and Economics,Shanghai 200433,China;Jiyang College of Zhejiang A&F University,Zhejiang Zhuji 311800,China)

机构地区：[1]上海财经大学统计与管理学院,上海200433 [2]浙江农林大学暨阳学院,浙江诸暨311800

出　　处：《数理统计与管理》2019年第1期40-48,共9页Journal of Applied Statistics and Management

基　　金：国家自然科学基金资助项目(71571113;71331006;91546202);上海财经大学创新团队支持计划(IRTSHUFE13122402)

摘　　要：本文讨论因变量缺失下部分线性变系数模型在误差项和解释变量都含有异常点时的稳健估计问题。首先用局部加权线性光滑方法得到非参数部分的稳健估计,然后再得到参数部分的估计,并证明参数和非参数估计量的渐近正态性。最后模拟研究有限样本下估计量的表现。In this paper, we consider the robust least-squares technique for estimating the parameter component and non-parameter component in the partially linear varying-coefficient models with missing response. Our proposal is resistant against outliers in the residuals and in the carriers as well. Using local weighted linear smoothing method to get the robust estimators of the non-parameter component firstly, and then obtain the estimation of the parameter part, the asymptotic distribution and asymptotic properties of the proposed estimators are discussed. Finally, a simulation study is conducted to examine the finite sample properties of the proposed estimators.

关 键 词：随机缺失数据 稳健估计 加权线性光滑 渐近正态性 部分线性模型 变系数 

分 类 号：O212.7[理学—概率论与数理统计]