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Functional Censored Quantile Regression

Author

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  • Fei Jiang
  • Qing Cheng
  • Guosheng Yin
  • Haipeng Shen
Abstract
We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we conduct extensive simulation studies to evaluate the finite sample performance of our method. Finally, we analyze the functional relationship between ambulatory blood pressure trajectories and clinical outcome in stroke patients. The results reinforce the importance of the morning blood pressure surge phenomenon, whose effect has caught attention but remains controversial in the medical literature. Supplementary materials for this article are available online.

Suggested Citation

  • Fei Jiang & Qing Cheng & Guosheng Yin & Haipeng Shen, 2020. "Functional Censored Quantile Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(530), pages 931-944, April.
    • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:530:p:931-944
    DOI: 10.1080/01621459.2019.1602047
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    Cited by:

    1. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    2. Zexi Cai & Tony Sit, 2023. "On interquantile smoothness of censored quantile regression with induced smoothing," Biometrics, The International Biometric Society, vol. 79(4), pages 3549-3563, December.

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