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Modern variable selection for longitudinal semi-parametric models with missing data

Author

Listed:
  • J. Kowalski
  • S. Hao
  • T. Chen
  • Y. Liang
  • J. Liu
  • L. Ge
  • C. Feng
  • X. M. Tu

Abstract

Penalized methods for variable selection such as the Smoothly Clipped Absolute Deviation penalty have been increasingly applied to aid variable section in regression analysis. Much of the literature has focused on parametric models, while a few recent studies have shifted the focus and developed their applications for the popular semi-parametric, or distribution-free, generalized estimating equations (GEEs) and weighted GEE (WGEE). However, although the WGEE is composed of one main and one missing-data module, available methods only focus on the main module, with no variable selection for the missing-data module. In this paper, we develop a new approach to further extend the existing methods to enable variable selection for both modules. The approach is illustrated by both real and simulated study data.

Suggested Citation

  • J. Kowalski & S. Hao & T. Chen & Y. Liang & J. Liu & L. Ge & C. Feng & X. M. Tu, 2018. "Modern variable selection for longitudinal semi-parametric models with missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(14), pages 2548-2562, October.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:14:p:2548-2562
    DOI: 10.1080/02664763.2018.1426739
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