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A compound Rayleigh survival model and its application to randomly censored data

Author

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  • M. Ghitany

Abstract

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Suggested Citation

  • 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.
    • Handle: RePEc:spr:stpapr:v:42:y:2001:i:4:p:437-450
    DOI: 10.1007/s003620100072
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Francisco Louzada & Daniele C. T. Granzotto, 2016. "The transmuted log-logistic regression model: a new model for time up to first calving of cows," Statistical Papers, Springer, vol. 57(3), pages 623-640, September.

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