IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i10p1783-d428346.html
   My bibliography  Save this article

Estimating the Parameters of the Two-Parameter Rayleigh Distribution Based on Adaptive Type II Progressive Hybrid Censored Data with Competing Risks

Author

Listed:
  • Shuhan Liu

    (Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China)

  • Wenhao Gui

    (Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China)

Abstract

This paper attempts to estimate the parameters for the two-parameter Rayleigh distribution based on adaptive Type II progressive hybrid censored data with competing risks. Firstly, the maximum likelihood function and the maximum likelihood estimators are derived before the existence and uniqueness of the latter are proven. Further, Bayesian estimators are considered under symmetric and asymmetric loss functions, that is the squared error loss function, the LINEXloss function, and the general entropy loss function. As the Bayesian estimators cannot be obtained explicitly, the Lindley method is applied to compute the approximate Bayesian estimates. Finally, a simulation study is conducted, and a real dataset is analyzed for illustrative purposes.

Suggested Citation

  • Shuhan Liu & Wenhao Gui, 2020. "Estimating the Parameters of the Two-Parameter Rayleigh Distribution Based on Adaptive Type II Progressive Hybrid Censored Data with Competing Risks," Mathematics, MDPI, vol. 8(10), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1783-:d:428346
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/10/1783/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/10/1783/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hon Keung Tony Ng & Debasis Kundu & Ping Shing Chan, 2009. "Statistical analysis of exponential lifetimes under an adaptive Type‐II progressive censoring scheme," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(8), pages 687-698, December.
    2. Howlader, Hatem A. & Hossain, Anwar M., 2002. "Bayesian survival estimation of Pareto distribution of the second kind based on failure-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 301-314, January.
    3. Jung-In Seo & Jae-Woo Jeon & Suk-Bok Kang, 2016. "Exact Interval Inference for the Two-Parameter Rayleigh Distribution Based on the Upper Record Values," Journal of Probability and Statistics, Hindawi, vol. 2016, pages 1-5, December.
    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. Alexey Kudryavtsev & Oleg Shestakov, 2023. "Limit Distributions for the Estimates of the Digamma Distribution Parameters Constructed from a Random Size Sample," Mathematics, MDPI, vol. 11(8), pages 1-13, April.

    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. Hurairah Ahmed & Alabid Abdelhakim, 2020. "Beta transmuted Lomax distribution with applications," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 13-34, June.
    2. BuHamra, Sana S. & Al-Kandari, N.M.Noriah M. & Ahmed, S. E., 2004. "Inference concerning quantile for left truncated and right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 819-831, July.
    3. E. M. Almetwally & H. M. Almongy & M. K. Rastogi & M. Ibrahim, 2020. "Maximum Product Spacing Estimation of Weibull Distribution Under Adaptive Type-II Progressive Censoring Schemes," Annals of Data Science, Springer, vol. 7(2), pages 257-279, June.
    4. Refah Alotaibi & Ehab M. Almetwally & Qiuchen Hai & Hoda Rezk, 2022. "Optimal Test Plan of Step Stress Partially Accelerated Life Testing for Alpha Power Inverse Weibull Distribution under Adaptive Progressive Hybrid Censored Data and Different Loss Functions," Mathematics, MDPI, vol. 10(24), pages 1-24, December.
    5. Ferreira, Paulo H. & Ramos, Eduardo & Ramos, Pedro L. & Gonzales, Jhon F.B. & Tomazella, Vera L.D. & Ehlers, Ricardo S. & Silva, Eveliny B. & Louzada, Francisco, 2020. "Objective Bayesian analysis for the Lomax distribution," Statistics & Probability Letters, Elsevier, vol. 159(C).
    6. Raj Kamal Maurya & Yogesh Mani Tripathi & Chandrakant Lodhi & Manoj Kumar Rastogi, 2019. "On a generalized Lomax distribution," 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. 10(5), pages 1091-1104, October.
    7. Muqrin A. Almuqrin & Mukhtar M. Salah & Essam A. Ahmed, 2022. "Statistical Inference for Competing Risks Model with Adaptive Progressively Type-II Censored Gompertz Life Data Using Industrial and Medical Applications," Mathematics, MDPI, vol. 10(22), pages 1-38, November.
    8. Fen Jiang & Junmei Zhou & Jin Zhang, 2020. "Restricted minimum volume confidence region for Pareto distribution," Statistical Papers, Springer, vol. 61(5), pages 2015-2029, October.
    9. Abdalla Abdel-Ghaly & Hanan Aly & Elham Abdel-Rahman, 2023. "Bayesian Inference Under Ramp Stress Accelerated Life Testing Using Stan," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 132-174, May.
    10. Siyi Chen & Wenhao Gui, 2020. "Statistical Analysis of a Lifetime Distribution with a Bathtub-Shaped Failure Rate Function under Adaptive Progressive Type-II Censoring," Mathematics, MDPI, vol. 8(5), pages 1-21, April.
    11. Lee, Jaeyong & Kim, Yongdai, 2004. "A new algorithm to generate beta processes," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 441-453, October.
    12. Siqi Chen & Wenhao Gui, 2020. "Estimation of Unknown Parameters of Truncated Normal Distribution under Adaptive Progressive Type II Censoring Scheme," Mathematics, MDPI, vol. 9(1), pages 1-33, December.
    13. Rastogi, Manoj Kumar & Tripathi, Yogesh Mani, 2013. "Estimation using hybrid censored data from a two-parameter distribution with bathtub shape," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 268-281.
    14. Mehdi Jabbari Nooghabi, 2016. "Estimation of the Lomax Distribution in the Presence of Outliers," Annals of Data Science, Springer, vol. 3(4), pages 385-399, December.
    15. Ahmed Elshahhat & Refah Alotaibi & Mazen Nassar, 2022. "Inferences for Nadarajah–Haghighi Parameters via Type-II Adaptive Progressive Hybrid Censoring with Applications," Mathematics, MDPI, vol. 10(20), pages 1-19, October.
    16. Ahmed Hurairah & Abdelhakim Alabid, 2020. "Beta transmuted Lomax distribution with applications," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 13-34, June.
    17. Ahmed Elshahhat & Mazen Nassar, 2021. "Bayesian survival analysis for adaptive Type-II progressive hybrid censored Hjorth data," Computational Statistics, Springer, vol. 36(3), pages 1965-1990, September.
    18. O. E. Abo-Kasem & Ehab M. Almetwally & Wael S. Abu El Azm, 2023. "Inferential Survival Analysis for Inverted NH Distribution Under Adaptive Progressive Hybrid Censoring with Application of Transformer Insulation," Annals of Data Science, Springer, vol. 10(5), pages 1237-1284, October.
    19. Hassan Okasha & Yuhlong Lio & Mohammed Albassam, 2021. "On Reliability Estimation of Lomax Distribution under Adaptive Type-I Progressive Hybrid Censoring Scheme," Mathematics, MDPI, vol. 9(22), pages 1-38, November.
    20. Refah Alotaibi & Mazen Nassar & Ahmed Elshahhat, 2022. "Computational Analysis of XLindley Parameters Using Adaptive Type-II Progressive Hybrid Censoring with Applications in Chemical Engineering," Mathematics, MDPI, vol. 10(18), pages 1-24, September.

    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:8:y:2020:i:10:p:1783-:d:428346. 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.