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Subgroup analysis for high-dimensional functional regression

Author

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  • Zhang, Xiaochen
  • Zhang, Qingzhao
  • Ma, Shuangge
  • Fang, Kuangnan

Abstract

Subgroup analysis for scalar data has been well studied in the literature. However, less has been done on functional data, especially on high-dimensional functional regression. In this study, we develop a high-dimensional functional regression model for simultaneous estimation and subgroup identification for a heterogeneous population. Under mild conditions, we establish the estimation and selection consistency of the proposed estimators. The proposed analysis allows the number of functional predictors and number of subgroups to increase as the sample size increases. Simulation studies demonstrate satisfactory performance of the proposed method, and it is also illustrated through a real application.

Suggested Citation

  • Zhang, Xiaochen & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2022. "Subgroup analysis for high-dimensional functional regression," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:jmvana:v:192:y:2022:i:c:s0047259x22000914
    DOI: 10.1016/j.jmva.2022.105100
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    References listed on IDEAS

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    Cited by:

    1. Weirong Li & Wensheng Zhu, 2024. "Subgroup analysis with concave pairwise fusion penalty for ordinal response," Statistical Papers, Springer, vol. 65(6), pages 3327-3355, August.

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