论文标题
通过自适应显着性水平提高线性模型的可复制性
Increasing the Replicability for Linear Models via Adaptive Significance Levels
论文作者
论文摘要
我们提出了一个自适应α（I型误差），该α随着信息的增长而降低，用于比较嵌套线性模型的假设测试。在\ citet {pp2014}中已经提出了不太精致的改编，以比较一般I.I.D.型号。在本文中，我们介绍了精制版本，以比较嵌套线性模型。该校准可以解释为贝叶斯 - 非贝尔巴斯妥协，对贝叶斯因子的简单翻译在常见的术语上，导致统计一致性，最重要的是，这是迈出统计的一步，促进了可复制的科学发现。
We put forward an adaptive alpha (Type I Error) that decreases as the information grows, for hypothesis tests in which nested linear models are compared. A less elaborate adaptation was already presented in \citet{PP2014} for comparing general i.i.d. models. In this article we present refined versions to compare nested linear models. This calibration may be interpreted as a Bayes-non-Bayes compromise, of a simple translations of a Bayes Factor on frequentist terms that leads to statistical consistency, and most importantly, it is a step towards statistics that promotes replicable scientific findings.
