Empirical Bayesian Strategy for Sampling Plans with Warranty under Truncated Censoring  
Author Tzong-Ru Tsai

 
Co-Author(s) Y. L. Lio; Jyun-You Chiang

 
Abstract To reach an optimal decision of acceptance sampling for lifetime products, sampling plans are developed with a rebate warranty policy based on truncated censored data, whose lifetimes are Burr type XII distributed. The smallest sample size and acceptance number are determined to minimize the expected total cost, which consists of the test cost, experimental time cost, the cost of lot acceptance or rejection, and the warranty cost. A simple empirical Bayesian estimation procedure is proposed to estimate the unknown distribution parameter and hyper-parameters. Moreover, the Genetic Algorithm method is used for parameter estimation. An algorithm is presented to implement the proposed method. Monte Carlo simulations are conducted to evaluate the performance of the proposed empirical Bayesian estimation procedure with the Genetic Algorithm method.

 
Keywords Loss function, posterior density function, prior density function, truncated life test
   
    Article #:  22274
 
Proceedings of the 22nd ISSAT International Conference on Reliability and Quality in Design
August 4-6, 2016 - Los Angeles, California, U.S.A.