Missing responses at random in functional single index model for time series data

被引：13
|
作者：
Ling, Nengxiang [1 ]
Cheng, Lilei [1 ]
Vieu, Philippe [2 ]
Ding, Hui [3 ]
机构：
[1] Hefei Univ Technol, Sch Math, Hefei 230009, Peoples R China
[2] Univ Paul Sabatier, Inst Math, Toulouse, France
[3] Nanjing Univ Finance & Econ, Sch Econ, Nanjing 210023, Peoples R China
来源：
STATISTICAL PAPERS | 2022年 / 63卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Functional single index model; Uniform almost complete convergence rate; Asymptotic normality; Strong mixing dependence; Missing responses at random; CONDITIONAL DENSITY-ESTIMATION; ASYMPTOTIC NORMALITY; REGRESSION;
D O I：
10.1007/s00362-021-01251-2
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 first investigate the estimation of the functional single index regression model with missing responses at random for strong mixing time series data. More precisely, the uniform almost complete convergence rate and asymptotic normality of the estimator are obtained respectively under some general conditions. Then, some simulation studies are carried out to show the finite sample performances of the estimator. Finally, a real data analysis about the sea surface temperature is used to illustrate the effectiveness of our methodology.
引用
收藏
页码：665 / 692
页数：28
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