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Prediction intervals based on Gompertz doubly censored data

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Abstract

This article is concerned with the problem of deriving Bayesian prediction bounds for the Gompertz \([Gomp(\alpha ,\,\beta )]\) distribution. Based on doubly Type II censored data, Bayesian prediction bounds for both the future observations will be derived. Two different sampling schemes have been considered. A conjugate prior for the one parameter case, as well as a joint prior for the two parameters case are outlined. Some numerical examples are given to illustrate the results.

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Acknowledgments

The authors would express their deepest gratitude and appreciation the Chief Editor, the Associate Editor and one anonymous referee for carefully reading the previous version of this article and for their great help in improving the article.

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Authors and Affiliations

  1. Mathematics Department, Faculty of Science, Al-Azhar University, Assiut Branch, Asyût, 71524, Egypt

    S. F. Niazi Ali

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  1. S. F. Niazi Ali
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Correspondence to S. F. Niazi Ali.

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Niazi Ali, S.F. Prediction intervals based on Gompertz doubly censored data. Comput Stat 31, 227–246 (2016). https://doi.org/10.1007/s00180-015-0613-7

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  • DOI: https://doi.org/10.1007/s00180-015-0613-7

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