Functional single-index composite quantile regression

被引：4
|
作者：
Jiang, Zhiqiang [1 ,2 ]
Huang, Zhensheng [2 ]
Zhang, Jing [2 ]
机构：
[1] Anhui Polytech Univ, Sch Math Phys & Finance, Wuhu 241000, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Math & Stat, Nanjing 210094, Peoples R China
来源：
METRIKA | 2023年 / 86卷 / 05期
基金：
国家教育部科学基金资助;
关键词：
B-splines; Composite quantile regression; Convergence rates; Functional data analysis; Functional single-index model; ESTIMATORS; MODELS;
D O I：
10.1007/s00184-022-00887-w
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 functional single-index model is a very flexible semiparametric model when modeling the relationship between a scalar response and functional predictors. However, the efficiency of the model may be affected by non-normal errors. So, in this paper, we propose functional single index composite quantile regression. The unknown slope function and link function are estimated by using B-spline basis functions. The convergence rates of the estimators are established. Some simulation studies and an application of NIR spectroscopy dataset are presented to illustrate the performance of the proposed methodologies.
引用
收藏
页码：595 / 603
页数：9
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