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Subgroup detection based on partially linear additive individualized model with missing data in response

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  • Cai, Tingting
  • Li, Jianbo
  • Zhou, Qin
  • Yin, Songlou
  • Zhang, Riquan

Abstract

Based on partially linear additive individualized model, a fusion-penalized inverse probability weighted least squares method is proposed to detect the subgroup for missing data in response. Firstly, the B-spline technique is used to approximate the unknown additive individualized functions and then an inverse probability weighted quadratic loss function is established with fusion penalty on the difference of subject-wise B-spline coefficients. Secondly, minimization of such quadratic loss function leads to the estimation of linear regression parameters and individualized B spline coefficients. With a proper tuning parameter, some differences in penalty term are shrunk into zero and thus the corresponding subjects will be clustered into the same subgroup. Thirdly, a clustering method is developed to automatically determine the subgroup membership for the subjects with missing data. Fourthly, large sample properties of resulting estimates are given under some regular conditions. Finally, numerical studies are presented to illustrate the performance of the proposed subgroup detection method.

Suggested Citation

  • Cai, Tingting & Li, Jianbo & Zhou, Qin & Yin, Songlou & Zhang, Riquan, 2024. "Subgroup detection based on partially linear additive individualized model with missing data in response," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    • Handle: RePEc:eee:csdana:v:192:y:2024:i:c:s0167947323002219
    DOI: 10.1016/j.csda.2023.107910
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Juan Shen & Xuming He, 2015. "Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 303-312, March.
    3. Susan Wei & Michael R. Kosorok, 2013. "Latent Supervised Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 957-970, September.
    4. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    5. Hansheng Wang & Runze Li & Chih-Ling Tsai, 2007. "Tuning parameter selectors for the smoothly clipped absolute deviation method," Biometrika, Biometrika Trust, vol. 94(3), pages 553-568.
    6. Liu, Lili & Lin, Lu, 2019. "Subgroup analysis for heterogeneous additive partially linear models and its application to car sales data," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 239-259.
    7. Lihui Zhao & Lu Tian & Tianxi Cai & Brian Claggett & L. J. Wei, 2013. "Effectively Selecting a Target Population for a Future Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 527-539, June.
    8. Shujie Ma & Jian Huang, 2017. "A Concave Pairwise Fusion Approach to Subgroup Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(517), pages 410-423, January.
    Full references (including those not matched with items on IDEAS)

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