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arXiv · 2003.06938

Increasing the Replicability for Linear Models via Adaptive Significance Levels

D. Vélez·M. E. Pérez·L. R. Pericchi
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Abstract

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.

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D. Vélez, M. E. Pérez, L. R. Pericchi. 2020-03-15. Increasing the Replicability for Linear Models via Adaptive Significance Levels. https://arxiv.org/abs/2003.06938

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