Increasing the replicability for linear models via adaptive significance levels

D. Vélez (), M. E. Pérez () and L. R. Pericchi ()
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D. Vélez: University of Puerto Rico
M. E. Pérez: University of Puerto Rico
L. R. Pericchi: University of Puerto Rico

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2022, vol. 31, issue 3, No 9, 789 pages

Abstract: Abstract We put forward an adaptive $$\alpha $$ α (type I error) that decreases as the information grows for hypothesis tests comparing nested linear models. A less elaborate adaptation was presented in Pérez and Pericchi (Stat Probab Lett 85:20–24, 2014) for general i.i.d. models. The calibration proposed in this paper may be interpreted as a Bayes–non-Bayes compromise, of a simple translation of a Bayes factor on frequentist terms that leads to statistical consistency, and most importantly, it is a step toward statistics that promotes replicable scientific findings.

Keywords: p-Value calibration; Bayes factor; Linear model; Likelihood ratio; Adaptive $$\alpha $$ α; PBIC; 62C05; 62C10; 62J20 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11749-022-00803-4

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