IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v13y2022i4d10.1007_s13198-021-01520-1.html
   My bibliography  Save this article

Different methods of estimation in two parameter Geometric distribution with randomly censored data

Author

Listed:
  • Neha Goel

    (Ch. Charan Singh University)

  • Hare Krishna

    (Ch. Charan Singh University)

Abstract

Random censoring scheme has been extensively discussed in literature for several statistical distribution models. Most of these studies focus on continuous variables with range 0 to ∞. In this paper the lifetime and censoring time variables are assumed to be discrete and a minimum threshold is assumed as a location parameter for failure and censoring times. Here, we study a two parameter geometric distribution with location parameter µ and probability parameter θ using randomly censored data. The importance of the minimum time location parameter is highlighted over the no location parameter case with an example. Some classical estimation methods such as methods of moments, least squares, L-moments and maximum likelihood estimation (MLE) are discussed. Asymptotic confidence intervals for parameters are derived using MLEs. Expected time on test is obtained for the parameters. Bayes estimators are developed under generalized entropy loss function (GELF) assuming informative as well as non-informative priors of the parameters. Maximum likelihood and Bayes estimates under GELF are also developed for the reliability characteristics. Various estimation procedures are compared using a Monte Carlo simulation study. The effect and importance of the minimum threshold parameter is illustrated with a numerical data example.

Suggested Citation

  • Neha Goel & Hare Krishna, 2022. "Different methods of estimation in two parameter Geometric distribution with randomly censored data," 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. 13(4), pages 1652-1665, August.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01520-1
    DOI: 10.1007/s13198-021-01520-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01520-1
    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/s13198-021-01520-1?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. Sarhan, Ammar M. & Kundu, Debasis, 2008. "Bayes estimators for reliability measures in geometric distribution model using masked system life test data," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1821-1836, January.
    2. H. Krishna & N. Goel, 2018. "Classical and Bayesian inference in two parameter exponential distribution with randomly censored data," Computational Statistics, Springer, vol. 33(1), pages 249-275, March.
    3. Hare Krishna & Neha Goel, 2017. "Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution," Journal of Probability and Statistics, Hindawi, vol. 2017, pages 1-12, February.
    4. Muhammad Danish & Muhammad Aslam, 2013. "Bayesian estimation for randomly censored generalized exponential distribution under asymmetric loss functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(5), pages 1106-1119.
    5. M. E. Ghitany & S. Al-Awadhi, 2002. "Maximum likelihood estimation of Burr XII distribution parameters under random censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 955-965.
    6. Kapil Kumar & Indrajeet Kumar, 2019. "Estimation in Inverse Weibull Distribution Based on Randomly Censored Data," Statistica, Department of Statistics, University of Bologna, vol. 79(1), pages 47-74.
    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. Hare Krishna & Rajni Goel, 2024. "Inferences Based on Correlated Randomly Censored Gumbel’s Type-I Bivariate Exponential Distribution," Annals of Data Science, Springer, vol. 11(4), pages 1185-1207, August.
    2. H. Krishna & N. Goel, 2018. "Classical and Bayesian inference in two parameter exponential distribution with randomly censored data," Computational Statistics, Springer, vol. 33(1), pages 249-275, March.
    3. Neha Goel, 2018. "Estimation Methods in Clinical Trials with Randomly Censored Exponential Healing Times and Rayleigh Dropout Times," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 8(3), pages 61-68, October.
    4. Nadeem Akhtar & Sajjad Ahmad Khan & Emad A. A. Ismail & Fuad A. Awwad & Akbar Ali Khan & Taza Gul & Haifa Alqahtani, 2024. "Analyzing quantitative performance: Bayesian estimation of 3-component mixture geometric distributions based on Kumaraswamy prior," Statistical Papers, Springer, vol. 65(7), pages 4431-4451, September.
    5. Ilhan Usta, 2013. "Different estimation methods for the parameters of the extended Burr XII distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 397-414, February.
    6. Junru Ren & Wenhao Gui, 2021. "Inference and optimal censoring scheme for progressively Type-II censored competing risks model for generalized Rayleigh distribution," Computational Statistics, Springer, vol. 36(1), pages 479-513, March.
    7. Kapil Kumar, 2018. "Classical and Bayesian estimation in log-logistic distribution under random 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. 9(2), pages 440-451, April.
    8. Zang, Zhaoqi & Xu, Xiangdong & Yang, Chao & Chen, Anthony, 2018. "A closed-form estimation of the travel time percentile function for characterizing travel time reliability," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 228-247.
    9. Rajni Goel & Hare Krishna, 2024. "A study of accidental breakages in progressively type-ii censored lifetime experiments," 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. 15(6), pages 2105-2119, June.
    10. Abbasi, B. & Hosseinifard, S.Z. & Coit, D.W., 2010. "A neural network applied to estimate Burr XII distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 647-654.
    11. Francisco Louzada & Pedro Luiz Ramos, 2017. "A New Long-Term Survival Distribution," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 1(5), pages 104-109, May.
    12. Indrajeet Kumar & Kapil Kumar, 2022. "On estimation of $$P(V," 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. 13(1), pages 189-202, February.

    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:ijsaem:v:13:y:2022:i:4:d:10.1007_s13198-021-01520-1. 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.