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Posterior analysis, prediction and reliability in three-parameter weibull distributions

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  • Efthymios G. Tsionas

Abstract

The paper develops Bayesian analysis in the context of samples from three-parameter Weibull distributions and shows how to tackle the problems of prediction and estimation of reliability curves. As Johnson, Kotz and Balakrishnan ( 1994 ) mentioned, the prediction problems for the three-parameter Weibull model seem to be unresolved and is certainly worth looking into (p.671). Posterior analysis organized around Gibbs sampling is shown to perform well. An application to stock returns is used to illustrate the potential of the approach.

Suggested Citation

  • Efthymios G. Tsionas, 2000. "Posterior analysis, prediction and reliability in three-parameter weibull distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 29(7), pages 1435-1449, January.
    • Handle: RePEc:taf:lstaxx:v:29:y:2000:i:7:p:1435-1449
    DOI: 10.1080/03610920008832555
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    Cited by:

    1. Nafidi, A. & Bahij, M. & Achchab, B. & Gutiérrez-Sanchez, R., 2019. "The stochastic Weibull diffusion process: Computational aspects and simulation," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 575-587.
    2. Saralees Nadarajah, 2012. "Models for stock returns," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 411-424, February.
    3. Lubrano, Michel & Protopopescu, Camelia, 2004. "Density inference for ranking European research systems in the field of economics," Journal of Econometrics, Elsevier, vol. 123(2), pages 345-369, December.

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