Principal varying coefficient estimator for high-dimensional models

被引：2
|
作者：
Zhao, Weihua [1 ]
Zhang, Fode [2 ,3 ]
Wang, Xuejun [4 ]
Li, Rui [5 ]
Lian, Heng [6 ]
机构：
[1] Nantong Univ, Sch Sci, Nantong, Peoples R China
[2] Southwestern Univ Finance & Econ, Ctr Stat Res, Chengdu, Sichuan, Peoples R China
[3] Southwestern Univ Finance & Econ, Sch Stat, Chengdu, Sichuan, Peoples R China
[4] Anhui Univ, Sch Math Sci, Hefei, Anhui, Peoples R China
[5] Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai, Peoples R China
[6] City Univ Hong Kong, Dept Math, Kowloon Tong, Hong Kong, Peoples R China
来源：
STATISTICS | 2019年 / 53卷 / 06期
关键词：
Asymptotic properties; B-splines; sub-Gaussian distribution; ultra-high dimensionality; NONCONCAVE PENALIZED LIKELIHOOD; VARIABLE SELECTION; QUANTILE REGRESSION; EFFICIENT ESTIMATION; LINEAR-MODELS; SHRINKAGE;
D O I：
10.1080/02331888.2019.1663521
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 principal varying coefficient models in the high-dimensional setting, combined with variable selection, to reduce the effective number of parameters in semiparametric modelling. The estimation is based on B-splines approach. For the unpenalized estimator, we establish non-asymptotic bounds of the estimator and then establish the (asymptotic) local oracle property of the penalized estimator, as well as non-asymptotic error bounds. Monte Carlo studies reveal the favourable performance of the estimator and an application on a real dataset is presented.
引用
收藏
页码：1234 / 1250
页数：17
相关论文
共 50 条
  • [1] ON VARYING-COEFFICIENT INDEPENDENCE SCREENING FOR HIGH-DIMENSIONAL VARYING-COEFFICIENT MODELS
    Song, Rui
    Yi, Feng
    Zou, Hui
    [J]. STATISTICA SINICA, 2014, 24 (04) : 1735 - 1752
  • [2] Spline estimator for simultaneous variable selection and constant coefficient identification in high-dimensional generalized varying-coefficient models
    Lian, Heng
    Meng, Jie
    Zhao, Kaifeng
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2015, 141 : 81 - 103
  • [3] Variable selection in high-dimensional quantile varying coefficient models
    Tang, Yanlin
    Song, Xinyuan
    Wang, Huixia Judy
    Zhu, Zhongyi
    [J]. JOURNAL OF MULTIVARIATE ANALYSIS, 2013, 122 : 115 - 132
  • [4] Modified adaptive group lasso for high-dimensional varying coefficient models
    Wang, Mingqiu
    Kang, Xiaoning
    Tian, Guo-Liang
    [J]. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2022, 51 (11) : 6495 - 6510
  • [5] High-dimensional quantile varying-coefficient models with dimension reduction
    Weihua Zhao
    Rui Li
    Heng Lian
    [J]. Metrika, 2022, 85 : 1 - 19
  • [6] VARIABLE SELECTION AND ESTIMATION IN HIGH-DIMENSIONAL VARYING-COEFFICIENT MODELS
    Wei, Fengrong
    Huang, Jian
    Li, Hongzhe
    [J]. STATISTICA SINICA, 2011, 21 (04) : 1515 - 1540
  • [7] Robust and sparse learning of varying coefficient models with high-dimensional features
    Xiong, Wei
    Tian, Maozai
    Tang, Manlai
    Pan, Han
    [J]. JOURNAL OF APPLIED STATISTICS, 2023, 50 (16) : 3312 - 3336
  • [8] VARIABLE SELECTION FOR HIGH-DIMENSIONAL GENERALIZED VARYING-COEFFICIENT MODELS
    Lian, Heng
    [J]. STATISTICA SINICA, 2012, 22 (04) : 1563 - 1588
  • [9] High-dimensional quantile varying-coefficient models with dimension reduction
    Zhao, Weihua
    Li, Rui
    Lian, Heng
    [J]. METRIKA, 2022, 85 (01) : 1 - 19
  • [10] Structural identification and variable selection in high-dimensional varying-coefficient models
    Chen, Yuping
    Bai, Yang
    Fung, Wingkam
    [J]. JOURNAL OF NONPARAMETRIC STATISTICS, 2017, 29 (02) : 258 - 279
12345