Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:171

Article

Zhang, Shen^1,2  Zhao, Peixin³  Li, Gaorong¹  Xu, Wangli⁴ 

[1] Beijing Univ Technol, Beijing Inst Sci & Engn Comp, Beijing 100124, Peoples R China

[2] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

[3] Chongqing Technol & Business Univ, Coll Math & Stat, Chongqing 400067, Peoples R China

[4] Renmin Univ China, Sch Stat, Ctr Appl Stat, Beijing 100872, Peoples R China

关键词: Generalized estimating equation;    Generalized varying coefficient model;    Nonparametric independence screening;    Sure screening properties;    Ultra-high longitudinal data;   

DOI  :  10.1016/j.jmva.2018.11.002

来源: Elsevier

【 摘 要 】

In this paper, we propose a nonparametric independence screening method for sparse ultra-high dimensional generalized varying coefficient models with longitudinal data. Our methods combine the ideas of sure independence screening (SIS) in sparse ultrahigh dimensional generalized linear models and varying coefficient models with the marginal generalized estimating equation (GEE) method, called NIS-GEE, considering both the marginal correlation between response and covariates, and the subject correlation for variable screening. The corresponding iterative algorithm is introduced to enhance the performance of the proposed NIS-GEE method. Furthermore it is shown that, under some regularity conditions, the proposed NIS-GEE method enjoys the sure screening properties. Simulation studies and a real data analysis are used to assess the performance of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.

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