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講題: Optimal Bayesian Sampling Plans for Exponential DistributionsBased on Hybrid Censored Samples
演講者：梁大成 教授 (Department of Mathematics, Wayne State University, Detroit, MI, U.S.A.)
Abstract:
We study variable sampling plans for exponential distributions based on type-I hybrid censored samples. For this problem, two sampling plans based on the non-failure sample proportion and the conditional maximum likelihood estimator are proposed by Chen et al. (2004) and Lin et al. (2008), respectively. From the theoretic decision point of view, the preceding two sampling plans are not optimal due to their decision functions not being the Bayes decision functions. In this talk, we consider the decision theoretic approach, and the optimal Bayesian sampling plan based on sufficient statistics is derived under a general loss function. Furthermore, for the conjugate prior distribution, the closed-form formula of the Bayes decision rule can be obtained under either the linear or quadratic decision loss. The resulting Bayesian sampling plan has the minimum Bayes risk, and hence it is better than the sampling plans proposed by Chen et al. (2004) and Lin et al. (2008). Numerical comparisons are given and demonstrate that the performance of the proposed Bayesian sampling plan is superior to that of Chen et al. (2004) and Lin et al. (2008).
時間：2011年5月24日(星期二) 14：00 ∼ 16：00
地點：推廣大樓3樓9313室
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