Preposterior Analysis of Bayes Estimators of Mean Life
从行为主义贝叶斯视角研究寿命试验中模型误设对后验均值的影响，发现当真实模型失效率递增且采用固定停止规则时，后验均值随样本量增加；基于指数模型和自然共轭先验的贝叶斯估计，其贝叶斯风险在变异系数较小时更低。
In considering life test experiments from a behaviouristic Bayes point of view, one may speculate on the effect of model misspecification on the posterior mean under alternative stopping rulers. Of coures, under the life distribution model, we expect the mean of the posterior to equal the mean of the prior. However, if we think the model may have been misspecified, we would no longer expect this equality. It is shown that for Bayes estimators of mean life under the exponential model and general priors on mean life, we expect, based on a preposterior analysis, that the mean of the posterior will increase with sample size when the true model has an increasing faliure rate and certian fixed stopping rules are used. Also, we show that for Bayes estimators of mean life based on the natural conjugate prior for the exponential model and complete samples, the Bayes risk is less when the coefficient of variation is less than for the exponential model. Recommendations relative to use of life test sampling plans based on an exponential model are made.