Varying-coefficient partially functional linear quantile regression models

被引：17

作者：

Yu, Ping ^{[1
,2
]}

Du, Jiang ^{[1
,3
]}

Zhang, Zhongzhan ^{[1
,3
]}

机构：

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

[2] Shanxi Normal Univ, Sch Math & Comp Sci, Linfen 041000, Peoples R China

[3] Collaborat Innovat Ctr Capital Social Construct &, Beijing 100124, Peoples R China

来源：

JOURNAL OF THE KOREAN STATISTICAL SOCIETY | 2017年 / 46卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Functional data analysis; Varying-coefficient quantile model; Functional linear quantile regression model; B-splines; Functional principal component analysis; VARIABLE SELECTION; CONVERGENCE-RATES; ESTIMATORS; METHODOLOGY; PREDICTION;

D O I：

10.1016/j.jkss.2017.02.001

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：

In this paper, we introduce a new varying-coefficient partially functional linear quantile regression model, which combines varying-coefficient quantile regression model with functional linear quantile regression model. The functional principal component basis and regression splines are employed to estimate the slope function and varying-coefficient functions, respectively, and the convergence rates of the estimators are obtained under some regularity conditions. Simulations and an illustrative real example are presented. (C) 2017 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.

引用

页码：462 / 475

页数：14

共 41 条

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