论文标题
电子价值：针对精确统计假设的完全贝叶斯的重要性措施及其研究计划
The e-value: A Fully Bayesian Significance Measure for Precise Statistical Hypotheses and its Research Program
论文作者
论文摘要
本文对电子价值进行了调查，即统计意义措施，即观察数据，X所提供的证据，以支持统计假设H，或相反的统计假设，或相反，给定X的认知价值。$ e $ value的认知价值和伴随的20个研究的核心核心，该研究的核心是构成的，该研究的核心是构成核心，该计划是启动的，该计划是启动的。到目前为止，全世界已有200多个出版物所引用。 电子价值和FBST符合贝叶斯推论的最佳原则，包括可能性原理，完全不变性，渐近一致性等。此外，它们在需要比较或构成不同统计学模型的不同假设中表现出强大的逻辑或代数属性。此外，他们毫不费力地容纳了尖锐或精确的假设的情况，这种情况通常需要临时和复杂的程序。最后，FBST具有出色的鲁棒性和可靠性特征，在统计建模和操作研究的许多实际应用中，超出了假设的传统检验。
This article gives a survey of the e-value, a statistical significance measure a.k.a. the evidence rendered by observational data, X, in support of a statistical hypothesis, H, or, the other way around, the epistemic value of H given X. The $e$-value and the accompanying FBST, the Full Bayesian Significance Test, constitute the core of a research program that was started at IME-USP, is being developed by over 20 researchers worldwide, and has, so far, been referenced by over 200 publications. The e-value and the FBST comply with the best principles of Bayesian inference, including the likelihood principle, complete invariance, asymptotic consistency, etc. Furthermore, they exhibit powerful logic or algebraic properties in situations where one needs to compare or compose distinct hypotheses that can be formulated either in the same or in different statistical models. Moreover, they effortlessly accommodate the case of sharp or precise hypotheses, a situation where alternative methods often require ad hoc and convoluted procedures. Finally, the FBST has outstanding robustness and reliability characteristics, outperforming traditional tests of hypotheses in many practical applications of statistical modeling and operations research.
