IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p2003-d1131023.html
   My bibliography  Save this article

Statistical Inference of Two Gamma Distributions under the Joint Type-II Censoring Scheme

Author

Listed:
  • Leijia Ding

    (School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China)

  • Wenhao Gui

    (School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China)

Abstract

The joint Type-II censoring scheme is a useful model when carrying out comparative lifecycle tests of units from various production lines. This article takes into account the estimation problem of the joint Type-II censoring data coming from two Gamma distributions with the same shape parameter but various scale parameters. The maximum likelihood estimators of the parameters from Gamma populations and asymptotic confidence intervals based on the observed Fisher information matrix are obtained. Bootstrap methods are also applied to construct confidence intervals. The Metropolis–Hastings algorithm is considered to draw Markov Chain Monte Carlo samples when computing Bayesian estimates as well as establishing the corresponding credible intervals. Monte Carlo simulations are adopted to compare the performance of the estimates. Finally, two real engineering datasets are analyzed.

Suggested Citation

  • Leijia Ding & Wenhao Gui, 2023. "Statistical Inference of Two Gamma Distributions under the Joint Type-II Censoring Scheme," Mathematics, MDPI, vol. 11(9), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2003-:d:1131023
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/2003/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/2003/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yahia Abdel-Aty, 2017. "Exact likelihood inference for two populations from two-parameter exponential distributions under joint Type-II censoring," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 9026-9041, September.
    2. Hirose, Hideo, 1995. "Maximum likelihood parameter estimation in the three-parameter gamma distribution," Computational Statistics & Data Analysis, Elsevier, vol. 20(4), pages 343-354, October.
    3. Jeon, Yongho & Kim, Joseph H.T., 2013. "A gamma kernel density estimation for insurance loss data," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 569-579.
    4. Balakrishnan, N. & Ling, M.H., 2014. "Gamma lifetimes and one-shot device testing analysis," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 54-64.
    5. Balakrishnan, N. & Rasouli, Abbas, 2008. "Exact likelihood inference for two exponential populations under joint Type-II censoring," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2725-2738, January.
    6. Rashad M. EL-Sagheer, 2018. "Estimation of parameters of Weibull–Gamma distribution based on progressively censored data," Statistical Papers, Springer, vol. 59(2), pages 725-757, June.
    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. Hanan Haj Ahmad & Dina A. Ramadan & Mohamed Aboshady, 2024. "Analyzing Chemical Decay in Environmental Nanomaterials Using Gamma Distribution with Hybrid Censoring Scheme," Mathematics, MDPI, vol. 12(23), pages 1-17, November.

    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. Yahia Abdel-Aty & Mohamed Kayid & Ghadah Alomani, 2023. "Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations," Mathematics, MDPI, vol. 11(9), pages 1-11, May.
    2. Ouimet, Frédéric & Tolosana-Delgado, Raimon, 2022. "Asymptotic properties of Dirichlet kernel density estimators," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    3. Bo-Hong Wu & Hirofumi Michimae & Takeshi Emura, 2020. "Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty–copula model," Computational Statistics, Springer, vol. 35(4), pages 1525-1552, December.
    4. Amel Abd-El-Monem & Mohamed S. Eliwa & Mahmoud El-Morshedy & Afrah Al-Bossly & Rashad M. EL-Sagheer, 2023. "Statistical Analysis and Theoretical Framework for a Partially Accelerated Life Test Model with Progressive First Failure Censoring Utilizing a Power Hazard Distribution," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    5. Chunmei Zhang & Tao Cong & Wenhao Gui, 2023. "Order-Restricted Inference for Generalized Inverted Exponential Distribution under Balanced Joint Progressive Type-II Censored Data and Its Application on the Breaking Strength of Jute Fibers," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
    6. Shu-Fei Wu & Meng-Zong Song, 2023. "Experimental Design for Progressive Type I Interval Censoring on the Lifetime Performance Index of Chen Lifetime Distribution," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    7. Julian Wells, Julian, 2007. "The rate of profit as a random variable," MPRA Paper 98235, University Library of Munich, Germany.
    8. Ling, M.H. & Hu, X.W., 2020. "Optimal design of simple step-stress accelerated life tests for one-shot devices under Weibull distributions," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    9. Ruhul Ali Khan & Murari Mitra, 2021. "Estimation issues in the Exponential–Logarithmic model under hybrid censoring," Statistical Papers, Springer, vol. 62(1), pages 419-450, February.
    10. Wu, Shuo-Jye & Hsu, Chu-Chun & Huang, Syuan-Rong, 2020. "Optimal designs and reliability sampling plans for one-shot devices with cost considerations," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    11. N. Balakrishnan & E. Castilla & N. Martín & L. Pardo, 2019. "Robust estimators for one-shot device testing data under gamma lifetime model with an application to a tumor toxicological data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(8), pages 991-1019, November.
    12. Qin, Shuidan & Wang, Bing Xing & Wu, Wenhui & Ma, Chao, 2022. "The prediction intervals of remaining useful life based on constant stress accelerated life test data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 747-755.
    13. Zhu, Xiaojun & Balakrishnan, N., 2022. "One-shot device test data analysis using non-parametric and semi-parametric inferential methods and applications," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    14. Ming Han, 2025. "Bayesian estimation and posterior risk under the generalized weighted squared error loss function and its applications," Statistical Papers, Springer, vol. 66(1), pages 1-26, January.
    15. Wenhui Zhang & Yongmin Su & Ruimin Ke & Xinqiang Chen, 2018. "Evaluating the influential priority of the factors on insurance loss of public transit," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    16. Balakrishnan, N. & So, H.Y. & Ling, M.H., 2015. "EM algorithm for one-shot device testing with competing risks under exponential distribution," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 129-140.
    17. Man-Ho Ling & Narayanaswamy Balakrishnan & Chenxi Yu & Hon Yiu So, 2021. "Inference for One-Shot Devices with Dependent k -Out-of- M Structured Components under Gamma Frailty," Mathematics, MDPI, vol. 9(23), pages 1-24, November.
    18. 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).
    19. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
    20. Franko, Mitja & Nagode, Marko, 2015. "Probability density function of the equivalent stress amplitude using statistical transformation," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 118-125.

    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:gam:jmathe:v:11:y:2023:i:9:p:2003-:d:1131023. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.