Time-varying feature selection for longitudinal analysis

Lan Xue, Xinxin Shu, Peibei Shi, Colin O. Wu, Annie Qu

Research output: Contribution to journalArticle

Abstract

We propose time-varying coefficient model selection and estimation based on the spline approach, which is capable of capturing time-dependent covariate effects. The new penalty function utilizes local-region information for varying-coefficient estimation, in contrast to the traditional model selection approach focusing on the entire region. The proposed method is extremely useful when the signals associated with relevant predictors are time-dependent, and detecting relevant covariate effects in the local region is more scientifically relevant than those of the entire region. Our simulation studies indicate that the proposed model selection incorporating local features outperforms the global feature model selection approaches. The proposed method is also illustrated through a longitudinal growth and health study from National Heart, Lung, and Blood Institute.

Original languageEnglish (US)
Pages (from-to)156-170
Number of pages15
JournalStatistics in Medicine
Volume39
Issue number2
DOIs
StatePublished - Jan 30 2020

Keywords

  • SCAD
  • adaptive lasso
  • group penalization
  • model selection
  • polynomial spline
  • truncated L-penalty

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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  • Cite this

    Xue, L., Shu, X., Shi, P., Wu, C. O., & Qu, A. (2020). Time-varying feature selection for longitudinal analysis. Statistics in Medicine, 39(2), 156-170. https://doi.org/10.1002/sim.8412