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Competing Risks Quantile Regression

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  • Peng, Limin
  • Fine, Jason P.

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  • Peng, Limin & Fine, Jason P., 2009. "Competing Risks Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1440-1453.
    • Handle: RePEc:bes:jnlasa:v:104:i:488:y:2009:p:1440-1453
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    Citations

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    Cited by:

    1. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    2. Jin-Jian Hsieh & Hong-Rui Wang, 2018. "Quantile regression based on counting process approach under semi-competing risks data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 395-419, April.
    3. Xiaoyan Sun & Limin Peng & Yijian Huang & HuiChuan J. Lai, 2016. "Generalizing Quantile Regression for Counting Processes With Applications to Recurrent Events," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 145-156, March.
    4. Jing Pan & Yuan Yu & Yong Zhou, 2018. "Nonparametric independence feature screening for ultrahigh-dimensional survival data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(7), pages 821-847, October.
    5. Lo, Simon M.S. & Stephan, Gesine & Wilke, Ralf, 2012. "Estimating the Latent Effect of Unemployment Benefits on Unemployment Duration," IZA Discussion Papers 6650, Institute of Labor Economics (IZA).
    6. Bernd Fitzenberger & Roger Koenker & José Machado & Blaise Melly, 2022. "Economic applications of quantile regression 2.0," Empirical Economics, Springer, vol. 62(1), pages 1-6, January.
    7. Lee, Minjung & Han, Junhee, 2016. "Covariate-adjusted quantile inference with competing risks," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 57-63.
    8. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    9. Jin-Jian Hsieh & Cheng-Chih Hsieh, 2023. "Quantile Regression Based on the Weighted Approach with Dependent Truncated Data," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    10. Ruosha Li & Limin Peng, 2011. "Quantile Regression for Left-Truncated Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 67(3), pages 701-710, September.
    11. Ying Cui & Limin Peng, 2022. "Assessing dynamic covariate effects with survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 675-699, October.
    12. Wenbin Lu & Lexin Li, 2011. "Sufficient Dimension Reduction for Censored Regressions," Biometrics, The International Biometric Society, vol. 67(2), pages 513-523, June.
    13. Erqian Li & Jianxin Pan & Manlai Tang & Keming Yu & Wolfgang Karl Härdle & Xiaowen Dai & Maozai Tian, 2023. "Weighted Competing Risks Quantile Regression Models and Variable Selection," Mathematics, MDPI, vol. 11(6), pages 1-23, March.
    14. Ruosha Li & Xuelin Huang & Jorge Cortes, 2016. "Quantile residual life regression with longitudinal biomarker measurements for dynamic prediction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 755-773, November.
    15. Huijuan Ma & Limin Peng & Zhumin Zhang & HuiChuan J. Lai, 2018. "Generalized accelerated recurrence time model for multivariate recurrent event data with missing event type," Biometrics, The International Biometric Society, vol. 74(3), pages 954-965, September.
    16. Simon M.S. Lo & Ralf A. Wilke, 2011. "Identifiability and estimation of the sign of a covariate effect in the competing risks model," Discussion Papers 11/03, University of Nottingham, School of Economics.
    17. Li, Ruosha & Peng, Limin, 2014. "Varying coefficient subdistribution regression for left-truncated semi-competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 65-78.

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