The Effective Sample Size

James Berger, M. J. Bayarri and L. R. Pericchi

Econometric Reviews, 2014, vol. 33, issue 1-4, 197-217

Abstract: Model selection procedures often depend explicitly on the sample size n of the experiment. One example is the Bayesian information criterion (BIC) criterion and another is the use of Zellner--Siow priors in Bayesian model selection. Sample size is well-defined if one has i.i.d real observations, but is not well-defined for vector observations or in non-i.i.d. settings; extensions of critera such as BIC to such settings thus requires a definition of effective sample size that applies also in such cases. A definition of effective sample size that applies to fairly general linear models is proposed and illustrated in a variety of situations. The definition is also used to propose a suitable 'scale' for default proper priors for Bayesian model selection.

Date: 2014
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DOI: 10.1080/07474938.2013.807157

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