The Effective Sample Size
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.
Year of publication: |
2014
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Authors: | Berger, James ; Bayarri, M. J. ; Pericchi, L. R. |
Published in: |
Econometric Reviews. - Taylor & Francis Journals, ISSN 0747-4938. - Vol. 33.2014, 1-4, p. 197-217
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Publisher: |
Taylor & Francis Journals |
Saved in:
Online Resource
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