淡江大學機構典藏:Item 987654321/110017
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/110017


    Title: Empirical Bayesian strategy for sampling plans with warranty under truncated censoring
    Authors: Jyun-You Chiang;Y.L. Lio;Tzong-Ru Tsai
    Keywords: Genetic algorithm;loss function;posterior density function;prior density function;truncated life test
    Date: 2016-09-28
    Issue Date: 2017-03-17 02:11:03 (UTC+8)
    Publisher: World Scientific Publishing Co. Pte. Ltd.
    Abstract: To reach an optimal acceptance sampling decision for products, whose lifetimes are Burr type XII distribution, sampling plans are developed with a rebate warranty policy based on truncated censored data. 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 new method, which combines a simple empirical Bayesian method and the genetic algorithm (GA) method, named the EB-GA method, is proposed to estimate the unknown distribution parameter and hyper-parameters. The parameters of the GA are determined through using an optimal Taguchi design procedure to reduce the subjectivity of parameter determination. An algorithm is presented to implement the EB-GA method. The application of the proposed method is illustrated by an example. Monte Carlo simulation results show that the EB-GA method works well for parameter estimation in terms of small bias and mean square error.
    Relation: International Journal of Reliability, Quality and Safety Engineering 23(5), p.1650021
    DOI: 10.1142/S0218539316500212
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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