Optimal Bayesian sampling plan for censored competing risks data. Issue 5 (24th March 2023)

• Record Type: Journal Article
• Authors: Prajapati, Deepak; Mitra, Sharmishtha; Kundu, Debasis; Pal, Ayan
• Is Part Of: Journal of statistical computation and simulation. Volume 93:Issue 5(2023)
• Page Start: 775
• Page End: 799
• Publication Date: 2023-03-24
• Subjects: Bayes risk -- exponential distribution -- Weibull distribution -- type-II censoring scheme -- type-i hybrid censoring scheme; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2022.2120993
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : There is a substantial amount of literature in the area of acceptance sampling plan with censored lifetime data. However, the optimality of a Bayesian sampling plan in the presence of competing risks has not been considered so far. In this paper, first, the Bayesian sampling plans (BSP) for Type-II and Type-I hybrid censoring schemes are discussed in presence of competing risks when the lifetime distribution is exponential. The closed-form expression of the Bayes decision function is obtained analytically for a linear loss function. Then we consider the Weibull distribution with an unknown shape parameter under Type-I hybrid censoring scheme in presence of competing risks to obtain the BSP. However, the Bayes decision function cannot be obtained in closed-form for a general loss function, and in such cases, a numerical algorithm is proposed. As an illustration, in the exponential case, a quadratic loss function, and in Weibull case, a non-polynomial loss function, are considered for the application of the proposed numerical approach to obtain the optimum BSPs using the Bayes decision function.