Nonparametric quantile regression estimation for functional data with responses missing at random

被引：5

作者：

Xu, Dengke ^{[1
]}

Du, Jiang ^{[2
]}

机构：

[1] Zhejiang Agr & Forestry Univ, Dept Stat, Hangzhou 311300, Peoples R China

[2] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

来源：

METRIKA | 2020年 / 83卷 / 08期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Quantile regression; Functional data analysis; Missing at random; Inverse probability weighting estimator; Asymptotic normality;

D O I：

10.1007/s00184-020-00769-z

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：

This paper presents the nonparametric quantile regression estimation for the regression function operator when the functional data with the responses missing at random are considered. Then, the large sample properties of the proposed estimator are established under some mild conditions. Finally, a simulation study is conducted to investigate the finite sample properties of the proposed method.

引用

页码：977 / 990

页数：14

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