Objective Bayes and conditional inference in exponential families
研究了多参数指数族中关于典范参数的条件频率推断的客观贝叶斯方法，推导出后验贝叶斯分位数匹配条件频率覆盖率的条件，并比较了客观贝叶斯方法与参数自助法和解析方法。
Objective Bayes methodology is considered for conditional frequentist inference about a canonical parameter in a multi-parameter exponential family. A condition is derived under which posterior Bayes quantiles match the conditional frequentist coverage to a higher-order approximation in terms of the sample size. This condition is on the model, not on the prior, and it ensures that any first-order probability matching prior in the unconditional sense automatically yields higher-order conditional probability matching. Objective Bayes methods are compared to parametric bootstrap and analytic methods for higher-order conditional frequentist inference.