PARTIALLY LINEAR ADDITIVE FUNCTIONAL REGRESSION

被引：2
|
作者：
Liu, Xiaohui [1 ]
Lu, Wenqi [2 ]
Lian, Heng [3 ]
Liu, Yuzi [1 ]
Zhu, Zhongyi [2 ]
机构：
[1] Jiangxi Univ Finance & Econ, Sch Stat, Nanchang, Jiangxi, Peoples R China
[2] Fudan Univ, Sch Management, Dept Stat, Shanghai 200433, Peoples R China
[3] City Univ Hong Kong, Dept Math, Hong Kong, Peoples R China
来源：
STATISTICA SINICA | 2022年 / 32卷
基金：
中国博士后科学基金;
关键词：
Convergence rate; functional data; penalization; RKHS; EFFICIENT ESTIMATION; MODELS; CONVERGENCE; SELECTION; SINGLE;
D O I：
10.5705/ss.202020.0418
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：
We consider a novel partially linear additive functional regression model in which both a functional predictor and some scalar predictors appear. The functional part has a semiparametric continuously additive form, while the scalar predictors appear in the linear part. The functional part has the optimal convergence rate, and the asymptotic normality of the nonfunctional part is also shown. Simulations and an empirical analysis of a Covid-19 data set demonstrate the performance of the proposed estimator.
引用
收藏
页码：2199 / 2216
页数：18
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