Optimal Sample Size in Type-II Progressive Censoring Using a Bayesian Prediction Approach

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Elham Basiri Elham Basiri; S.M.T.K. MirMostafaee S.M.T.K. MirMostafaee

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF OPERATIONS RESEARCH Year:2023 | Volume:14 | Issue:2 Page(s): 47-56

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Title

Optimal Sample Size in Type-II Progressive Censoring Using a Bayesian Prediction Approach

Author(s)

Elham Basiri Elham Basiri | S.M.T.K. MirMostafaee S.M.T.K. MirMostafaee | Issue Writer Certificate

Keywords

optimization problem

general entropy loss function

Bayes risk function

prediction.

Abstract

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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