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A generalization of the compound rayleigh distribution: using a bayesian method on cancer survival times

Author

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  • A. Bekker
  • J.J.J. Roux
  • P.J. Mosteit

Abstract

In this paper the generalized compound Rayleigh model, exhibiting flexible hazard rate, is high¬lighted. This makes it attractive for modelling survival times of patients showing characteristics of a random hazard rate. The Bayes estimators are derived for the parameters of this model and some survival time parameters from a right censored sample. This is done with respect to conjugate and discrete priors on the parameters of this model, under the squared error loss function, Varian's asymmetric linear-exponential (linex) loss function and a weighted linex loss function. The future survival time of a patient is estimated under these loss functions. A Monte Carlo simu¬lation procedure is used where closed form expressions of the estimators cannot be obtained. An example illustrates the proposed estimators for this model.

Suggested Citation

  • A. Bekker & J.J.J. Roux & P.J. Mosteit, 2000. "A generalization of the compound rayleigh distribution: using a bayesian method on cancer survival times," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 29(7), pages 1419-1433, January.
    • Handle: RePEc:taf:lstaxx:v:29:y:2000:i:7:p:1419-1433
    DOI: 10.1080/03610920008832554
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    Cited by:

    1. Abdullah Fathi & Al-Wageh A. Farghal & Ahmed A. Soliman, 2022. "Bayesian and Non-Bayesian Inference for Weibull Inverted Exponential Model under Progressive First-Failure Censoring Data," Mathematics, MDPI, vol. 10(10), pages 1-19, May.
    2. Gayan Warahena-Liyanage & Broderick Oluyede & Thatayaone Moakofi & Whatmore Sengweni, 2023. "The New Exponentiated Half Logistic-Harris-G Family of Distributions with Actuarial Measures and Applications," Stats, MDPI, vol. 6(3), pages 1-29, July.

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