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Simultaneous subgroup identification and variable selection for high dimensional data

Author

Listed:
  • Huicong Yu

    (University of Science and Technology of China)

  • Jiaqi Wu

    (University of Science and Technology of China)

  • Weiping Zhang

    (University of Science and Technology of China
    University of Science and Technology of China)

Abstract

The high dimensionality of genetic data poses many challenges for subgroup identification, both computationally and theoretically. This paper proposes a double-penalized regression model for subgroup analysis and variable selection for heterogeneous high-dimensional data. The proposed approach can automatically identify the underlying subgroups, recover the sparsity, and simultaneously estimate all regression coefficients without prior knowledge of grouping structure or sparsity construction within variables. We optimize the objective function using the alternating direction method of multipliers with a proximal gradient algorithm and demonstrate the convergence of the proposed procedure. We show that the proposed estimator enjoys the oracle property. Simulation studies demonstrate the effectiveness of the novel method with finite samples, and a real data example is provided for illustration.

Suggested Citation

  • Huicong Yu & Jiaqi Wu & Weiping Zhang, 2024. "Simultaneous subgroup identification and variable selection for high dimensional data," Computational Statistics, Springer, vol. 39(6), pages 3181-3205, September.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:6:d:10.1007_s00180-023-01436-3
    DOI: 10.1007/s00180-023-01436-3
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    References listed on IDEAS

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