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

Author

Listed:
  • Zhang, Shen
  • Zhao, Peixin
  • Li, Gaorong
  • Xu, Wangli

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.

Suggested Citation

  • Zhang, Shen & Zhao, Peixin & Li, Gaorong & Xu, Wangli, 2019. "Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 37-52.
    • Handle: RePEc:eee:jmvana:v:171:y:2019:i:c:p:37-52
    DOI: 10.1016/j.jmva.2018.11.002
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    References listed on IDEAS

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