Bayesian optimal life‐testing plan under the balanced two sample type‐II progressive censoring scheme. (18th February 2020)

• Record Type: Journal Article
• Authors: Mondal, Shuvashree; Bhattacharya, Ritwik; Pradhan, Biswabrata; Kundu, Debasis
• Is Part Of: Applied stochastic models in business and industry. Volume 36:Number 4(2020)
• Page Start: 628
• Page End: 640
• Publication Date: 2020-02-18
• Subjects: Bayesian D‐optimality criterion -- joint censoring scheme -- optimal life‐testing plan -- precision criterion -- variable neighborhood search; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2519
• Languages: English
• ISSNs: 1524-1904

Abstract

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.