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In sensitivity experiments, the response is binary and each experimental unit has a critical stimulus level that cannot be observed directly. It is often of interest to estimate extreme quantiles of the distribution of these critical stimulus levels over the tested products. For this purpose a new sequential scheme is proposed with some commonly used models. By using the bootstrap repeated-sampling principle, reasonable prior distributions based on a historic data set are specified. Then, a Bayesian strategy for the sequential procedure is provided and the estimator is given. Further, a high order approximation for such an estimator is explored and its consistency is proven. A simulation study shows that the proposed method gives superior performances over the existing methods. 相似文献
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以先验信息为基础,在给定的总试验量下,以系统故障率估计的均方误差最小为优化目标,给出解析算法和蒙特卡洛模拟两种计算方法,确定了不同环境下试验量的最优分配方案.最后,将该优化试验设计方法应用于某型号车载速射迫炮在三种温度环境下的优化试验设计. 相似文献
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