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Bayesian optimal life‐testing plan under the balanced two sample type‐II progressive censoring scheme

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  • Shuvashree Mondal
  • Ritwik Bhattacharya
  • Biswabrata Pradhan
  • Debasis Kundu

Abstract

Joint progressive censoring schemes are quite useful to conduct comparative life‐testing experiment of different competing products. Recently, Mondal and Kundu (“A New Two Sample Type‐II Progressive Censoring Scheme,” Commun Stat‐Theory Methods; 2018) introduced a joint progressive censoring scheme on two samples known as the balanced joint progressive censoring (BJPC) scheme. Optimal planning of such progressive censoring scheme is an important issue to the experimenter. This article considers optimal life‐testing plan under the BJPC scheme using the Bayesian precision and D‐optimality criteria, assuming that the lifetimes follow Weibull distribution. In order to obtain the optimal BJPC life‐testing plans, one needs to carry out an exhaustive search within the set of all admissible plans under the BJPC scheme. However, for large sample size, determination of the optimal life‐testing plan is difficult by exhaustive search technique. A metaheuristic algorithm based on the variable neighborhood search method is employed for computation of the optimal life‐testing plan. Optimal plans are provided under different scenarios. The optimal plans depend upon the values of the hyperparameters of the prior distribution. The effect of different prior information on optimal scheme is studied.

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  • Shuvashree Mondal & Ritwik Bhattacharya & Biswabrata Pradhan & Debasis Kundu, 2020. "Bayesian optimal life‐testing plan under the balanced two sample type‐II progressive censoring scheme," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(4), pages 628-640, July.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:4:p:628-640
    DOI: 10.1002/asmb.2519
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    1. Sonal Budhiraja & Biswabrata Pradhan, 2017. "Computing optimum design parameters of a progressive type I interval censored life test from a cost model," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(5), pages 494-506, September.
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