Statistical inference for varying-coefficient partially linear errors-in-variables models with missing data

被引:12
|
作者
Xu, Hong-Xia [1 ]
Fan, Guo-Liang [2 ,3 ]
Wu, Cheng-Xin [4 ]
Chen, Zhen-Long [5 ]
机构
[1] Shanghai Maritime Univ, Dept Math, Shanghai, Peoples R China
[2] Shanghai Maritime Univ, Sch Econ & Management, Shanghai, Peoples R China
[3] Renmin Univ China, Inst Stat & Big Data, Beijing, Peoples R China
[4] Huangshan Univ, Sch Math & Stat, Huangshan, Peoples R China
[5] Zhejiang Gongshang Univ, Sch Stat & Math, Hangzhou, Zhejiang, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Empirical likelihood; errors-in-variables; inverse probability weighted; missing data; varying coefficient partially linear model; EMPIRICAL LIKELIHOOD;
D O I
10.1080/03610926.2018.1517216
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The purpose of this paper is twofold. First, we investigate estimations in varying-coefficient partially linear errors-in-variables models with covariates missing at random. However, the estimators are often biased due to the existence of measurement errors, the bias-corrected profile least-squares estimator and local liner estimators for unknown parametric and coefficient functions are obtained based on inverse probability weighted method. The asymptotic properties of the proposed estimators both for the parameter and nonparametric parts are established. Second, we study asymptotic distributions of an empirical log-likelihood ratio statistic and maximum empirical likelihood estimator for the unknown parameter. Based on this, more accurate confidence regions of the unknown parameter can be constructed. The methods are examined through simulation studies and illustrated by a real data analysis.
引用
收藏
页码:5621 / 5636
页数:16
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