Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin
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Andrew Gelman
Abstract
For many decades, statisticians have made attempts to prepare the Bayesian omelette without breaking the Bayesian eggs; that is, to obtain probabilistic likelihood-based inferences without relying on informative prior distributions. A recent example is Murray Aitkin´s recent book, Statistical Inference, which presents an approach to statistical hypothesis testing based on comparisons of posterior distributions of likelihoods under competing models. Aitkin develops and illustrates his method using some simple examples of inference from iid data and two-way tests of independence. We analyze in this note some consequences of the inferential paradigm adopted therein, discussing why the approach is incompatible with a Bayesian perspective and why we do not find it relevant for applied work.
© by Oldenbourg Wissenschaftsverlag, München, Germany
Articles in the same Issue
- Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin
- Comments on the review of Statistical Inference
- Loss-based risk measures
- A harmonic function approach to Nash-equilibria of Kifer-type stopping games
- A note on the biasedness and unbiasedness of two-sample Kolmogorov–Smirnov test
Articles in the same Issue
- Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin
- Comments on the review of Statistical Inference
- Loss-based risk measures
- A harmonic function approach to Nash-equilibria of Kifer-type stopping games
- A note on the biasedness and unbiasedness of two-sample Kolmogorov–Smirnov test
