A General Framework for Model-Based Statistics
本文提出一个基于充分性和辅助性扩展概念的通用框架，其基本定理用一个方程统一了决策理论、模型检验和条件推断的参考分布，填补了经验贝叶斯统计中充分性和条件推断概念的空白。
This paper presents a general framework for model-based statistics. The framework is based on extended concepts of sufficiency and ancillarity. The fundamental theorem of the new theory exhibits in a single equation the reference distributions for decision theory, model checking, and conditional inference for a general statistical model. The theorem includes the equations upon which Bayesian and frequentist statistics are founded. The general theory also fills two gaps previously missing from empirical Bayesian statistics: concepts of sufficiency and conditional inference.