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Increasing the replicability for linear models via adaptive significance levels

Author

Listed:
  • D. Vélez

    (University of Puerto Rico)

  • M. E. Pérez

    (University of Puerto Rico)

  • L. R. Pericchi

    (University of Puerto Rico)

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.

Suggested Citation

  • D. Vélez & M. E. Pérez & L. R. Pericchi, 2022. "Increasing the replicability for linear models via adaptive significance levels," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 771-789, September.
    • Handle: RePEc:spr:testjl:v:31:y:2022:i:3:d:10.1007_s11749-022-00803-4
    DOI: 10.1007/s11749-022-00803-4
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    References listed on IDEAS

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    4. Pérez, María-Eglée & Pericchi, Luis Raúl, 2014. "Changing statistical significance with the amount of information: The adaptive α significance level," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 20-24.
    5. M. J. Bayarri & James O. Berger & Woncheol Jang & Surajit Ray & Luis R. Pericchi & Ingmar Visser, 2019. "Prior-based Bayesian information criterion," Statistical Theory and Related Fields, Taylor & Francis Journals, vol. 3(1), pages 2-13, January.
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