IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v194y2022icp285-307.html
   My bibliography  Save this article

Analysis of dependent left-truncated and right-censored competing risks data with partially observed failure causes

Author

Listed:
  • Wang, Liang
  • Tripathi, Yogesh Mani
  • Dey, Sanku
  • Zhang, Chunfang
  • Wu, Ke

Abstract

In this paper, studies of left-truncated and right-censored data with dependent competing risks are discussed. When the latent lifetime of competing failure causes is modeled by a bivariate Marshall–Olkin Weibull distribution, a dependent competing risks model is established for left-truncated and right-censored data with partially observed causes of failure, and classical and Bayesian estimations are presented in consequence. Maximum likelihood estimators along with the associated existence and uniqueness of the model parameters are established, and the asymptotic likelihood theory is used to construct approximate confidence intervals based on asymptotic theory. Further, based on general flexible priors, Bayes estimators and the associated highest posterior density credible intervals of the parameters are also obtained, and an importance sampling technique is used to generate samples from the posterior distribution and in turn to compute the Bayes estimates. Finally, extensive Monte-Carlo simulations are carried out to investigate the performances of our results and two real-life examples are analyzed to show the applicabilities of the proposed methods. The numerical results show that our proposed methods work satisfactory.

Suggested Citation

  • Wang, Liang & Tripathi, Yogesh Mani & Dey, Sanku & Zhang, Chunfang & Wu, Ke, 2022. "Analysis of dependent left-truncated and right-censored competing risks data with partially observed failure causes," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 194(C), pages 285-307.
    • Handle: RePEc:eee:matcom:v:194:y:2022:i:c:p:285-307
    DOI: 10.1016/j.matcom.2021.11.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037847542100433X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2021.11.026?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. Balakrishnan, N. & Mitra, Debanjan, 2012. "Left truncated and right censored Weibull data and likelihood inference with an illustration," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4011-4025.
    2. Kundu, Debasis & Gupta, Arjun K., 2013. "Bayes estimation for the Marshall–Olkin bivariate Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 271-281.
    3. Hwang, Yi-Ting & Wang, Chun-chao, 2008. "A goodness of fit test for left-truncated and right-censored data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2420-2425, October.
    4. Kundu, Debasis & Mitra, Debanjan & Ganguly, Ayon, 2017. "Analysis of left truncated and right censored competing risks data," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 12-26.
    5. Feizjavadian, S.H. & Hashemi, R., 2015. "Analysis of dependent competing risks in the presence of progressive hybrid censoring using Marshall–Olkin bivariate Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 19-34.
    6. Kundu, Debasis & Gupta, Rameshwar D., 2009. "Bivariate generalized exponential distribution," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 581-593, April.
    7. Sarhan, Ammar M. & Hamilton, David C. & Smith, B., 2010. "Statistical analysis of competing risks models," Reliability Engineering and System Safety, Elsevier, vol. 95(9), pages 953-962.
    8. Zhang, Fode & Shi, Yimin & Wang, Ruibing, 2017. "Geometry of the q-exponential distribution with dependent competing risks and accelerated life testing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 552-565.
    9. Xiaodong Luo & Wei Yann Tsai, 2009. "Nonparametric estimation for right-censored length-biased data: a pseudo-partial likelihood approach," Biometrika, Biometrika Trust, vol. 96(4), pages 873-886.
    10. Chunfang Zhang & Yimin Shi, 2017. "Optimum simple accelerated life tests based on progressively Type-I hybrid censoring," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 849-856, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Chunfang & Wang, Liang & Bai, Xuchao & Huang, Jianan, 2022. "Bayesian reliability analysis for copula based step-stress partially accelerated dependent competing risks model," Reliability Engineering and System Safety, Elsevier, vol. 227(C).
    2. Zhiyuan Zuo & Liang Wang & Yuhlong Lio, 2022. "Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations," Energies, MDPI, vol. 16(1), pages 1-25, December.

    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. Hirofumi Michimae & Takeshi Emura, 2022. "Likelihood Inference for Copula Models Based on Left-Truncated and Competing Risks Data from Field Studies," Mathematics, MDPI, vol. 10(13), pages 1-15, June.
    2. Zhiyuan Zuo & Liang Wang & Yuhlong Lio, 2022. "Reliability Estimation for Dependent Left-Truncated and Right-Censored Competing Risks Data with Illustrations," Energies, MDPI, vol. 16(1), pages 1-25, December.
    3. Muhammed, Hiba Z., 2020. "On a bivariate generalized inverted Kumaraswamy distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    4. Ahmadi, Jafar & Doostparast, Mahdi & Parsian, Ahmad, 2012. "Estimation with left-truncated and right censored data: A comparison study," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1391-1400.
    5. N. Davarzani & L. Golparvar & A. Parsian & R. Peeters, 2017. "Estimation on dependent right censoring scheme in an ordinary bivariate geometric distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(8), pages 1369-1384, June.
    6. Kundu, Debasis & Gupta, Arjun K., 2014. "On bivariate Weibull-Geometric distribution," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 19-29.
    7. Debasis Kundu, 2022. "Bivariate Semi-parametric Singular Family of Distributions and its Applications," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 846-872, November.
    8. M. S. Eliwa & M. El-Morshedy, 2019. "Bivariate Gumbel-G Family of Distributions: Statistical Properties, Bayesian and Non-Bayesian Estimation with Application," Annals of Data Science, Springer, vol. 6(1), pages 39-60, March.
    9. Ke Wu & Liang Wang & Li Yan & Yuhlong Lio, 2021. "Statistical Inference of Left Truncated and Right Censored Data from Marshall–Olkin Bivariate Rayleigh Distribution," Mathematics, MDPI, vol. 9(21), pages 1-24, October.
    10. Ying Zhou & Liang Wang & Tzong-Ru Tsai & Yogesh Mani Tripathi, 2023. "Estimation of Dependent Competing Risks Model with Baseline Proportional Hazards Models under Minimum Ranked Set Sampling," Mathematics, MDPI, vol. 11(6), pages 1-30, March.
    11. Fatih Kızılaslan & Mustafa Nadar, 2018. "Estimation of reliability in a multicomponent stress–strength model based on a bivariate Kumaraswamy distribution," Statistical Papers, Springer, vol. 59(1), pages 307-340, March.
    12. Ranjan, Rakesh & Sen, Rijji & Upadhyay, Satyanshu K., 2021. "Bayes analysis of some important lifetime models using MCMC based approaches when the observations are left truncated and right censored," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Chengping Gong & Chengxiu Ling, 2018. "Robust Estimations for the Tail Index of Weibull-Type Distribution," Risks, MDPI, vol. 6(4), pages 1-15, October.
    14. Yu-Jen Cheng & Mei-Cheng Wang, 2012. "Estimating Propensity Scores and Causal Survival Functions Using Prevalent Survival Data," Biometrics, The International Biometric Society, vol. 68(3), pages 707-716, September.
    15. Xifan Song & Ziyu Xiong & Wenhao Gui, 2022. "Parameter Estimation of Exponentiated Half-Logistic Distribution for Left-Truncated and Right-Censored Data," Mathematics, MDPI, vol. 10(20), pages 1-26, October.
    16. Calabrese, Raffaella & Osmetti, Silvia Angela, 2019. "A new approach to measure systemic risk: A bivariate copula model for dependent censored data," European Journal of Operational Research, Elsevier, vol. 279(3), pages 1053-1064.
    17. Coolen-Maturi, Tahani & Coolen, Frank P.A., 2014. "Nonparametric predictive inference for combined competing risks data," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 87-97.
    18. Jing Cai & Yimin Shi & Bin Liu, 2017. "Statistical analysis for masked system life data from Marshall‐Olkin Weibull distribution under progressive hybrid censoring," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(6), pages 490-501, September.
    19. Coolen-Maturi, Tahani, 2014. "Nonparametric predictive pairwise comparison with competing risks," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 146-153.
    20. Fang, Chen & Cui, Lirong, 2021. "Balanced Systems by Considering Multi-state Competing Risks Under Degradation Processes," Reliability Engineering and System Safety, Elsevier, vol. 205(C).

    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:eee:matcom:v:194:y:2022:i:c:p:285-307. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    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.