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Nonparametric Additive Regression for High-Dimensional Group Testing Data

Author

Listed:
  • Xinlei Zuo

    (School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China)

  • Juan Ding

    (School of Mathematics, Hohai University, Nanjing 210098, China)

  • Junjian Zhang

    (School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China)

  • Wenjun Xiong

    (School of Mathematics and Statistics, Guangxi Normal University, Guilin 541004, China)

Abstract

Group testing has been verified as a cost-effective and time-efficient approach, where the individual samples are pooled with a predefined group size for subsequent testing. Recent research has explored the integration of covariate information to improve the modeling of the group testing data. While existing works for high-dimensional data primarily focus on parametric models, this study considers a more flexible generalized nonparametric additive model. Nonlinear components are approximated using B-splines and model estimation under the sparsity assumption is derived employing group lasso. Theoretical results demonstrate that our method selects the true model with a high probability and provides consistent estimates. Numerical studies are conducted to illustrate the good performance of our proposed method, using both simulated and real data.

Suggested Citation

  • Xinlei Zuo & Juan Ding & Junjian Zhang & Wenjun Xiong, 2024. "Nonparametric Additive Regression for High-Dimensional Group Testing Data," Mathematics, MDPI, vol. 12(5), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:686-:d:1346583
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    References listed on IDEAS

    as
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