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On Optimal Progressive Censoring Schemes for Normal Distribution

Author

Listed:
  • U. H. Salemi

    (Amirkabir University of Technology)

  • S. Rezaei

    (Amirkabir University of Technology)

  • Y. Si

    (University of Manchester)

  • S. Nadarajah

    (University of Manchester)

Abstract

Selection of optimal progressive censoring schemes for the normal distribution is discussed according to maximum likelihood estimation and best linear unbiased estimation. The selection is based on variances of the estimators of the two parameters of the normal distribution. The extreme left censoring scheme is shown to be an optimal progressive censoring scheme. The usual type-II right censoring case is shown to be the worst progressive censoring scheme for estimating the scale parameter. It can greatly increase the variance of estimators.

Suggested Citation

  • U. H. Salemi & S. Rezaei & Y. Si & S. Nadarajah, 2018. "On Optimal Progressive Censoring Schemes for Normal Distribution," Annals of Data Science, Springer, vol. 5(4), pages 637-658, December.
    • Handle: RePEc:spr:aodasc:v:5:y:2018:i:4:d:10.1007_s40745-018-0156-1
    DOI: 10.1007/s40745-018-0156-1
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    References listed on IDEAS

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    1. N. Balakrishnan, 2007. "Progressive censoring methodology: an appraisal," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 211-259, August.
    2. Balakrishnan, N. & Childs, A. & Chandrasekar, B., 2002. "An efficient computational method for moments of order statistics under progressive censoring," Statistics & Probability Letters, Elsevier, vol. 60(4), pages 359-365, December.
    3. N. Balakrishnan, 2007. "Rejoinder on: Progressive censoring methodology: an appraisal," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(2), pages 290-296, August.
    4. Cramer, Erhard & Schmiedt, Anja Bettina, 2011. "Progressively Type-II censored competing risks data from Lomax distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1285-1303, March.
    5. Burkschat, M. & Cramer, E. & Kamps, U., 2006. "On optimal schemes in progressive censoring," Statistics & Probability Letters, Elsevier, vol. 76(10), pages 1032-1036, May.
    6. Pareek, Bhuvanesh & Kundu, Debasis & Kumar, Sumit, 2009. "On progressively censored competing risks data for Weibull distributions," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4083-4094, October.
    7. Balakrishnan, N. & Burkschat, Marco & Cramer, Erhard & Hofmann, Glenn, 2008. "Fisher information based progressive censoring plans," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 366-380, December.
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    Cited by:

    1. Mohamed A. W. Mahmoud & Mohamed G. M. Ghazal & Hossam M. M. Radwan, 2023. "Bayesian Estimation and Optimal Censoring of Inverted Generalized Linear Exponential Distribution Using Progressive First Failure Censoring," Annals of Data Science, Springer, vol. 10(2), pages 527-554, April.
    2. Uoseph Hamdi Salemi & Esmaile Khorram & Yuancheng Si & Saralees Nadarajah, 2020. "Sensitivity analysis of censoring schemes in progressively type-II right censored order statistics," OPSEARCH, Springer;Operational Research Society of India, vol. 57(1), pages 163-189, March.

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