On the Foundations of Statistical Inference
统计推断的基础AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要讨论统计推断基础与一般原则，未限定到任何所列经济计量细分领域。
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统计推断的基础
- 研究问题
- 研究条件实验参照框架对统计推断意味着什么，以及条件性原则与似然原则如何关联，并如何影响传统推断的解释。
- 方法与思路
- 作者形式化条件性原则并作数学推导，讨论似然原则及由其启发的显著性、置信水平和标准误重新解释，并联系贝叶斯方法。
- 材料陈述的贡献
- 摘要称推导的一般后果包括似然原则，并发展和举例说明若干内在显著性、置信水平及标准误概念，但未给出完整论证条件。
- 阅读关注点
- 应核对条件性原则的精确定义、推导所用其他原则或辅助条件，以及新概念与传统重复抽样解释之间如何对应。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，不能确认条件性原则单独足以推出似然原则；也无法评估完整论证及相关概念的适用边界。
原文摘要
The concept of conditional experimental frames of reference has a significance for the general theory of statistical inference which has been emphasized by R. A. Fisher, D. R. Cox, J. W. Tukey, and others. This concept is formulated as a principle of conditionality, from which some general consequences are deduced mathematically. These include the likelihood principle, which has not hitherto been very widely accepted, in contrast with the conditionality concept which many statisticians are inclined to accept for purposes of “informative inference.” The likelihood principle states that the “evidential meaning” of experimental results is characterized fully by the likelihood function, without other reference to the structure of an experiment, in contrast with standard methods in which significance and confidence levels are based on the complete experimental model. The principal writers supporting the likelihood principle have been Fisher and G. A. Barnard, in addition to Bayesian writers for whom it represents the “directly empirical” part of their standpoint. The likelihood principle suggests certain systematic reinterpretations and revisions of standard methods, including “intrinsic significance and confidence levels” and “intrinsic standard errors,” which are developed and illustrated. The close relations between non-Bayesian likelihood methods and Bayesian methods are discussed.