Increasing the replicability for linear models via adaptive significance levels

• D. Vélez ^[1] ; M. E. Pérez ^[2] ; L. R. Pericchi ^[2]
  1. [1] Statistical Institute and Computerized Information Systems, Faculty of Business Administration, University of Puerto Rico
  2. [2] Department of Mathematics, Faculty of Natural Sciences, University of Puerto Rico
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 31, Nº. 3, 2022, págs. 771-789
• Idioma: inglés
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• Resumen

  • We put forward an adaptive 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.