IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v43y2016i4p921-938.html
   My bibliography  Save this article

Semiparametric Quantile Regression Analysis of Right-censored and Length-biased Failure Time Data with Partially Linear Varying Effects

Author

Listed:
  • Xuerong Chen
  • Yeqian Liu
  • Jianguo Sun
  • Yong Zhou

Abstract

No abstract is available for this item.

Suggested Citation

  • Xuerong Chen & Yeqian Liu & Jianguo Sun & Yong Zhou, 2016. "Semiparametric Quantile Regression Analysis of Right-censored and Length-biased Failure Time Data with Partially Linear Varying Effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 921-938, December.
    • Handle: RePEc:bla:scjsta:v:43:y:2016:i:4:p:921-938
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/sjos.12221
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jianwen Cai & Jianqing Fan & Jiancheng Jiang & Haibo Zhou, 2008. "Partially linear hazard regression with varying coefficients for multivariate survival data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 141-158, February.
    2. Chiung-Yu Huang & Jing Qin, 2011. "Nonparametric estimation for length-biased and right-censored data," Biometrika, Biometrika Trust, vol. 98(1), pages 177-186.
    3. Shen, Yu & Ning, Jing & Qin, Jing, 2009. "Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1192-1202.
    4. Jing Ning & Jing Qin & Yu Shen, 2011. "Buckley–James-Type Estimator with Right-Censored and Length-Biased Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1369-1378, December.
    5. Jing Qin & Yu Shen, 2010. "Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox Model," Biometrics, The International Biometric Society, vol. 66(2), pages 382-392, June.
    6. Zhang, Hao Helen & Cheng, Guang & Liu, Yufeng, 2011. "Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1099-1112.
    7. Wei Yann Tsai, 2009. "Pseudo-partial likelihood for proportional hazards models with biased-sampling data," Biometrika, Biometrika Trust, vol. 96(3), pages 601-615.
    8. Wolkewitz, Martin & Allignol, Arthur & Schumacher, Martin & Beyersmann, Jan, 2010. "Two Pitfalls in Survival Analyses of Time-Dependent Exposure: A Case Study in a Cohort of Oscar Nominees," The American Statistician, American Statistical Association, vol. 64(3), pages 205-211.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu Shen & Jing Ning & Jing Qin, 2017. "Nonparametric and semiparametric regression estimation for length-biased survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 3-24, January.
    2. Ma, Huijuan & Zhang, Feipeng & Zhou, Yong, 2015. "Composite estimating equation approach for additive risk model with length-biased and right-censored data," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 45-53.
    3. Yifan He & Yong Zhou, 2020. "Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 761-788, October.
    4. Lin, Cunjie & Zhou, Yong, 2014. "Analyzing right-censored and length-biased data with varying-coefficient transformation model," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 45-63.
    5. Zexi Cai & Tony Sit, 2020. "Censored quantile regression model with time‐varying covariates under length‐biased sampling," Biometrics, The International Biometric Society, vol. 76(4), pages 1201-1215, December.
    6. Chengbo Li & Yong Zhou, 2021. "The estimation for the general additive–multiplicative hazard model using the length-biased survival data," Statistical Papers, Springer, vol. 62(1), pages 53-74, February.
    7. Lin, Cunjie & Zhou, Yong, 2016. "Semiparametric varying-coefficient model with right-censored and length-biased data," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 119-144.
    8. Chi Hyun Lee & Jing Ning & Yu Shen, 2018. "Analysis of restricted mean survival time for length†biased data," Biometrics, The International Biometric Society, vol. 74(2), pages 575-583, June.
    9. Zhiping Qiu & Jing Qin & Yong Zhou, 2016. "Composite Estimating Equation Method for the Accelerated Failure Time Model with Length-biased Sampling Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 396-415, June.
    10. Chi Hyun Lee & Jing Ning & Yu Shen, 2019. "Model diagnostics for the proportional hazards model with length-biased data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 79-96, January.
    11. Micha Mandel & Jacobo de Uña†à lvarez & David K. Simon & Rebecca A. Betensky, 2018. "Inverse probability weighted Cox regression for doubly truncated data," Biometrics, The International Biometric Society, vol. 74(2), pages 481-487, June.
    12. Zhang, Feipeng & Peng, Heng & Zhou, Yong, 2016. "Composite partial likelihood estimation for length-biased and right-censored data with competing risks," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 160-176.
    13. Jung-Yu Cheng & Shinn-Jia Tzeng, 2014. "Quantile regression of right-censored length-biased data using the Buckley–James-type method," Computational Statistics, Springer, vol. 29(6), pages 1571-1592, December.
    14. Zhang, Qiaozhen & Dai, Hongsheng & Fu, Bo, 2016. "A proportional hazards model for time-to-event data with epidemiological bias," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 224-236.
    15. Jieli Ding & Tsui-Shan Lu & Jianwen Cai & Haibo Zhou, 2017. "Recent progresses in outcome-dependent sampling with failure time data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 57-82, January.
    16. Lin, Cunjie & Zhou, Yong, 2014. "Inference for the treatment effects in two sample problems with right-censored and length-biased data," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 17-24.
    17. Shi, Jianhua & Ma, Huijuan & Zhou, Yong, 2018. "The nonparametric quantile estimation for length-biased and right-censored data," Statistics & Probability Letters, Elsevier, vol. 134(C), pages 150-158.
    18. Li Xun & Li Tao & Yong Zhou, 2020. "Estimators of quantile difference between two samples with length-biased and right-censored data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 409-429, June.
    19. Peng Liu & Yixin Wang & Yong Zhou, 2015. "Quantile residual lifetime with right-censored and length-biased data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(5), pages 999-1028, October.
    20. Chyong-Mei Chen & Pao-sheng Shen & Yi Liu, 2021. "On semiparametric transformation model with LTRC data: pseudo likelihood approach," Statistical Papers, Springer, vol. 62(1), pages 3-30, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:scjsta:v:43:y:2016:i:4:p:921-938. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.