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

Shen Zhang, Peixin Zhao, Gaorong Li and Wangli Xu

Journal of Multivariate Analysis, 2019, vol. 171, issue C, 37-52

Abstract: 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 ultra-high 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.

Keywords: Generalized estimating equation; Generalized varying coefficient model; Nonparametric independence screening; Sure screening properties; Ultra-high longitudinal data (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:171:y:2019:i:c:p:37-52

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DOI: 10.1016/j.jmva.2018.11.002

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