IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v36y2021i3d10.1007_s00180-020-00995-z.html
   My bibliography  Save this article

On consistency of the monotone NPMLE of survival function under the mixed case interval-censored model with left truncation

Author

Listed:
  • Chun-Lung Su

    (Tunghai University)

  • Pao-sheng Shen

    (Tunghai University)

Abstract

In some applications, it may be assumed or known that the survival function has a nondecreasing hazard function. The maximum likelihood estimator under this assumption is called the monotone nonparametric maximum likelihood estimator (MoNPMLE). In this article, we establish the conditional consistency of the MoNPMLE under the mixed case interval-censored model with left truncation. We also investigate the unconditional consistency of the MoNPMLE through simulation study. The Gradient Projection Method algorithm is used to obtain the MoNPMLE. Simulation results indicate that the MoNPMLE is consistent and outperforms the NPMLE.

Suggested Citation

  • Chun-Lung Su & Pao-sheng Shen, 2021. "On consistency of the monotone NPMLE of survival function under the mixed case interval-censored model with left truncation," Computational Statistics, Springer, vol. 36(3), pages 1871-1883, September.
    • Handle: RePEc:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-020-00995-z
    DOI: 10.1007/s00180-020-00995-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-020-00995-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-020-00995-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Pan, Wei & Chappell, Rick & Kosorok, Michael R., 1998. "On consistency of the monotone MLE of survival for left truncated and interval-censored data," Statistics & Probability Letters, Elsevier, vol. 38(1), pages 49-57, May.
    2. Anton Schick & Qiqing Yu, 2000. "Consistency of the GMLE with Mixed Case Interval‐Censored Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(1), pages 45-55, March.
    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. Pao-Sheng Shen, 2020. "Nonparametric estimators of survival function under the mixed case interval-censored model with left truncation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(3), pages 624-637, July.
    2. Pao-sheng Shen, 2013. "Regression analysis of interval censored and doubly truncated data with linear transformation models," Computational Statistics, Springer, vol. 28(2), pages 581-596, April.
    3. Peijie Wang & Hui Zhao & Jianguo Sun, 2016. "Regression analysis of case K interval‐censored failure time data in the presence of informative censoring," Biometrics, The International Biometric Society, vol. 72(4), pages 1103-1112, December.
    4. Qiqing Yu & George Wong & Linxiong Li, 2001. "Asymptotic Properties of Self-Consistent Estimators with Mixed Interval-Censored Data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(3), pages 469-486, September.
    5. Marra, Giampiero & Farcomeni, Alessio & Radice, Rosalba, 2021. "Link-based survival additive models under mixed censoring to assess risks of hospital-acquired infections," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    6. Wang, Yong, 2008. "Dimension-reduced nonparametric maximum likelihood computation for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2388-2402, January.
    7. Yuet-Yee Wong, Linda & Yu, Qiqing, 2007. "A bivariate interval censorship model for partnership formation," Journal of Multivariate Analysis, Elsevier, vol. 98(2), pages 370-383, February.
    8. Guadalupe Gómez & M. Calle & Ramon Oller, 2004. "Frequentist and Bayesian approaches for interval-censored data," Statistical Papers, Springer, vol. 45(2), pages 139-173, April.
    9. Pao-sheng Shen, 2011. "Nonparametric estimation with doubly censored and truncated data," Computational Statistics, Springer, vol. 26(1), pages 145-157, March.
    10. Li, Chenxi, 2016. "Cause-specific hazard regression for competing risks data under interval censoring and left truncation," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 197-208.
    11. Ma, Ling & Hu, Tao & Sun, Jianguo, 2016. "Cox regression analysis of dependent interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 79-90.
    12. Michael G. Hudgens & Marloes H. Maathuis & Peter B. Gilbert, 2007. "Nonparametric Estimation of the Joint Distribution of a Survival Time Subject to Interval Censoring and a Continuous Mark Variable," Biometrics, The International Biometric Society, vol. 63(2), pages 372-380, June.
    13. Yu, Shaohua & Yu, Qiqing & Wong, George Y.C., 2006. "Consistency of the generalized MLE of a joint distribution function with multivariate interval-censored data," Journal of Multivariate Analysis, Elsevier, vol. 97(3), pages 720-732, March.
    14. Sy Han Chiou & Gongjun Xu & Jun Yan & Chiung‐Yu Huang, 2018. "Semiparametric estimation of the accelerated mean model with panel count data under informative examination times," Biometrics, The International Biometric Society, vol. 74(3), pages 944-953, September.
    15. Qiqing Yu & Yuting Hsu & Kai Yu, 2014. "A necessary and sufficient condition for justifying non-parametric likelihood with censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(8), pages 995-1011, November.
    16. Li, Chenxi, 2016. "The Fine–Gray model under interval censored competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 327-344.
    17. Pao-sheng Shen, 2022. "Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring," Computational Statistics, Springer, vol. 37(1), pages 29-42, March.
    18. Michael G. Hudgens & Chenxi Li & Jason P. Fine, 2014. "Parametric likelihood inference for interval censored competing risks data," Biometrics, The International Biometric Society, vol. 70(1), pages 1-9, March.
    19. Pao-sheng Shen & Yingwei Peng & Hsin-Jen Chen & Chyong-Mei Chen, 2022. "Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(1), pages 68-88, January.
    20. Balakrishnan, N. & Zhao, Xingqiu, 2010. "A nonparametric test for the equality of counting processes with panel count data," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 135-142, January.

    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:spr:compst:v:36:y:2021:i:3:d:10.1007_s00180-020-00995-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.