inherent difficulties of non-Bayesian likelihood-based inference

Following a series of rejections of our discussion of Murray Aitkin’s book, Statistical Inference, discussion written with Andrew Gelman and Judith Rousseau, by the journals Bayesian Analysis, JASA (Book Reviews), and Electronic Journal of Statistics, we have received an encouraging review from the journal Statistics and Risk Modeling (with Applications on Finance and Insurance), formerly Statistics and Decisions. Since the main request was to broaden our perspective, we revised the paper towards a more global analysis of the issues raised by Murray’s book. For a start, the title got changed from the maybe provocative “Do we need an integrated Bayesian/likelihood inference?” into the slightly archaic “Inherent Difficulties of Non-Bayesian Likelihood-based Inference, as Revealed by an Examination of a Recent Book by Aitkin“. If only to explain why it is broader than a mere book review… For another, the paper also addresses similar criticisms to the deviance information criterion (DIC). Hopefully,  this revision will be considered more positively and turn into a discussion paper about this unBayesian use of Bayesian tools…

One Response to “inherent difficulties of non-Bayesian likelihood-based inference”

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