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
相关论文
共 50 条
  • [41] Dynamic single-index model for functional data
    Ma HaiQiang
    Bai Yang
    Zhu ZhongYi
    SCIENCE CHINA-MATHEMATICS, 2016, 59 (12) : 2561 - 2584
  • [42] Model Selection with Missing Data Embedded in Missing-at-Random Data
    Takai, Keiji
    Hayashi, Kenichi
    STATS, 2023, 6 (02): : 495 - 505
  • [43] Time series AR model parameter estimation with missing observation data
    Ding, Jie
    Chen, Xiaoming
    Ding, Feng
    2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 5632 - 5636
  • [44] Forecasting time series with missing data using Holt's model
    Bermudez, Jose D.
    Corberan-Vallet, Ana
    Vercher, Enriqueta
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2009, 139 (08) : 2791 - 2799
  • [45] Online Learning for Time Series Prediction of AR Model with Missing Data
    Yang, Haimin
    Pan, Zhisong
    Tao, Qing
    NEURAL PROCESSING LETTERS, 2019, 50 (03) : 2247 - 2263
  • [46] Online Learning for Time Series Prediction of AR Model with Missing Data
    Haimin Yang
    Zhisong Pan
    Qing Tao
    Neural Processing Letters, 2019, 50 : 2247 - 2263
  • [47] Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random
    Febrero-Bande, Manuel
    Galeano, Pedro
    Gonzalez-Manteiga, Wenceslao
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2019, 131 : 91 - 103
  • [48] THE ECONOMETRIC MODEL OF A RANDOM SYSTEM THAT GENERATES TIME SERIES DATA
    Dosescu, Tatiana Corina
    Raischi, Constantin
    ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, 2008, 42 (3-4): : 85 - 95
  • [49] The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series
    Sun, Xiwen
    Lu, Tieding
    Hu, Shunqiang
    Huang, Jiahui
    He, Xiaoxing
    Montillet, Jean-Philippe
    Ma, Xiaping
    Huang, Zhengkai
    REMOTE SENSING, 2023, 15 (14)
  • [50] Regression estimation for continuous-time functional data processes with missing at random response
    Chaouch, Mohamed
    Laib, Naamane
    JOURNAL OF NONPARAMETRIC STATISTICS, 2025, 37 (01) : 1 - 32
12345