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Statistical inference for two Lindley populations under balanced joint progressive type-II censoring scheme

Author

Listed:
  • Rajni Goel

    (Chaudhary Charan Singh University)

  • Hare Krishna

    (Chaudhary Charan Singh University)

Abstract

In order to conduct a comparative lifetime experiment in life testing and reliability theory, the joint censoring scheme has received immense popularity in the last decade. Recently, a new improved joint progressive censoring scheme has been introduced in statistical literature, known as balanced joint progressive censoring scheme. The present study deals with the statistical inferences for the balanced jointly progressive type-II censored two Lindley populations. Maximum likelihood estimators of the model parameters are derived and construction of the asymptotic confidence intervals based on the observed Fisher information matrix is discussed. From the Bayesian point of view, the posterior estimates of the unknown model parameters are calculated assuming the informative priors. A numerical study is carried out to evaluate the efficiency and performance of the proposed estimates. A real data set is analyzed to exemplify all the estimation techniques. Lastly, the criteria for an optimum censoring scheme are given.

Suggested Citation

  • Rajni Goel & Hare Krishna, 2022. "Statistical inference for two Lindley populations under balanced joint progressive type-II censoring scheme," Computational Statistics, Springer, vol. 37(1), pages 263-286, March.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:1:d:10.1007_s00180-021-01122-2
    DOI: 10.1007/s00180-021-01122-2
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    References listed on IDEAS

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    1. K. Shuvo Bakar, 2020. "Interpolation of daily rainfall data using censored Bayesian spatially varying model," Computational Statistics, Springer, vol. 35(1), pages 135-152, March.
    2. Cesar Augusto Taconeli & Suely Ruiz Giolo, 2020. "Maximum likelihood estimation based on ranked set sampling designs for two extensions of the Lindley distribution with uncensored and right-censored data," Computational Statistics, Springer, vol. 35(4), pages 1827-1851, December.
    3. A. Asgharzadeh & A. Fallah & M. Z. Raqab & R. Valiollahi, 2018. "Statistical inference based on Lindley record data," Statistical Papers, Springer, vol. 59(2), pages 759-779, June.
    4. Varian, Hal R., 1974. "Equity, envy, and efficiency," Journal of Economic Theory, Elsevier, vol. 9(1), pages 63-91, September.
    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. M. Mahdizadeh & E. Strzalkowska-Kominiak, 2017. "Resampling based inference for a distribution function using censored ranked set samples," Computational Statistics, Springer, vol. 32(4), pages 1285-1308, December.
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    Cited by:

    1. 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.
    2. Kundan Singh & Yogesh Mani Tripathi & Chandrakant Lodhi & Liang Wang, 2025. "Order Restricted Inference for a Generalized Family of Inverted Exponentiated Distributions with BPTC Scheme," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(2), pages 604-649, November.
    3. 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.

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