On the jeffreys-Lindley's Paradox
AbstractThis paper discusses the dual interpretation of the Jeffreys–Lindley’s paradox associated with Bayesian posterior probabilities and Bayes factors, both as a differentiation between frequentist and Bayesian statistics and as a pointer to the difficulty of using improper priors while testing. We stress the considerable impact of this paradox on the foundations of both classical and Bayesian statistics. While assessing existing resolutions of the paradox, we focus on a critical viewpoint of the paradox discussed by Spanos (2013) in the current journal
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Bibliographic InfoPaper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2013-46.
Date of creation: Dec 2013
Date of revision:
inference; Testing statistical hypotheses; Type I error; significance level; p-value;
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- Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
- Celeux, Gilles & El Anbari, Mohammed & Marin, Jean-Michel & Robert, Christian P., 2012. "Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation," Economics Papers from University Paris Dauphine 123456789/4911, Paris Dauphine University.
- Valen E. Johnson & David Rossell, 2010. "On the use of non-local prior densities in Bayesian hypothesis tests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 143-170.
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