Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients

Mu Yue*, Jialiang Li, Ming-Yen CHENG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

9 Citations (Scopus)
61 Downloads (Pure)


Varying-coefficient models are widely used to model nonparametric interaction and recently adopted to analyze longitudinal data measured repeatedly over time. We focus on high-dimensional longitudinal observations in this article. A novel two-step sparse boosting approach is proposed to carry out the variable selection and the model-based prediction. As a new machine learning tool, boosting provides seamless integration of model estimation and variable selection for complicated regression functions. Specifically, in the first step the sparse boosting technique assuming independence is applied to facilitate an initial estimate of the correlation structure while in the second step the estimated correlation structure is incorporated in the loss function of the sparse boosting algorithm. Extensive numerical examples illustrate the advantage of the two-step sparse boosting method. An application of yeast cell cycle gene expression data is further provided to demonstrate the proposed methodology.

Original languageEnglish
Pages (from-to)222-234
Number of pages13
JournalComputational Statistics and Data Analysis
Publication statusPublished - Mar 2019

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Longitudinal data
  • Minimum description length
  • Sparse boosting
  • Variable selection
  • Varying-coefficient model


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