The Effective Sample Size

Published

Journal Article

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. © 2014 Copyright Taylor and Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Berger, J; Bayarri, MJ; Pericchi, LR

Published Date

  • February 1, 2014

Published In

Volume / Issue

  • 33 / 1-4

Start / End Page

  • 197 - 217

Electronic International Standard Serial Number (EISSN)

  • 1532-4168

International Standard Serial Number (ISSN)

  • 0747-4938

Digital Object Identifier (DOI)

  • 10.1080/07474938.2013.807157

Citation Source

  • Scopus