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Classical and Bayesian inference in two parameter exponential distribution with randomly censored data

Author

Listed:
  • H. Krishna

    (Ch. Charan Singh University)

  • N. Goel

    (Ch. Charan Singh University)

Abstract

This paper deal with the classical and Bayesian estimation for two parameter exponential distribution having scale and location parameters with randomly censored data. The censoring time is also assumed to follow a two parameter exponential distribution with different scale but same location parameter. The main stress is on the location parameter in this paper. This parameter has not yet been studied with random censoring in literature. Fitting and using exponential distribution on the range $$(0, \infty )$$ ( 0 , ∞ ) , specially when the minimum observation in the data set is significantly large, will give estimates far from accurate. First we obtain the maximum likelihood estimates of the unknown parameters with their variances and asymptotic confidence intervals. Some other classical methods of estimation such as method of moment, L-moments and least squares are also employed. Next, we discuss the Bayesian estimation of the unknown parameters using Gibbs sampling procedures under generalized entropy loss function with inverted gamma priors and Highest Posterior Density credible intervals. We also consider some reliability and experimental characteristics and their estimates. A Monte Carlo simulation study is performed to compare the proposed estimates. Two real data examples are given to illustrate the importance of the location parameter.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0725-3
    DOI: 10.1007/s00180-017-0725-3
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    References listed on IDEAS

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    1. M. Ghitany, 2001. "A compound Rayleigh survival model and its application to randomly censored data," Statistical Papers, Springer, vol. 42(4), pages 437-450, October.
    2. 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.
    3. 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.
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    Citations

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    Cited by:

    1. 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.
    2. 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.

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