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Optimal Sample Size in Type-II Progressive Censoring Using a Bayesian Prediction Approach
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Elham Basiri   , S.M.T.K. MirMostafaee * |
| University of Mazandaran , m.mirmostafaee@umz.ac.ir |
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Abstract: (1246 Views) |
| This paper considers the progressively Type-II censoring and determines the optimal sample size using a Bayesian prediction approach. To this end, two criteria, namely the Bayes risk function of the point predictor for a future progressively censored order statistic and the designing cost of the experiment are considered. In the Bayesian prediction, the general entropy loss function is applied. We find the optimal sample size such that the Bayes risk function and the cost of the experiment do not exceed two pre-fixed values. To show the usefulness of the results, some numerical computations are presented. |
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| Keywords: optimization problem, general entropy loss function, Bayes risk function, prediction. |
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Full-Text [PDF 426 kb]
(2710 Downloads)
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Type of Study: Original |
Subject:
Discrete Optimization
Received: 2024/01/21 | Accepted: 2024/01/24 | Published: 2024/02/6
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