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Improper priors with well defined Bayes Factors

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Author Info
Rodney W. Strachan ()
Herman K. van Dijk

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Abstract

While some improper priors have attractive properties, it is generally claimed that Bartlett’s paradox implies that using improper priors for the parameters in alternative models results in Bayes factors that are not well defined, thus preventing model comparison in this case. In this paper we demonstrate, using well understood principles underlying what is already common practice, that this latter result is not generally true and so expand the class of priors that may be used for computing posterior odds to two classes of improper priors: the shrink age prior; and a prior based upon a nesting argument. Using a new representation of the issue of undefined Bayes factors, we develop classes of improper priors from which well defined Bayes factors result. However, as the use of such priors is not free of problems, we include discussion on the issues with using such priors for model comparison.

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Publisher Info
Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 05/4.

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Date of creation: Mar 2005
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Handle: RePEc:lec:leecon:05/4

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Keywords: Improper prior; Bayes factor; marginal likelihood; shrinkage prior; measure;

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  1. HOOGERHEIDE, Lennart F. & KAASHOEK, Johan F. & VAN DIJK, Herman K., 2005. "On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks," CORE Discussion Papers 2005029, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE). [Downloadable!]
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