A design-based approximation to the Bayes Information Criterion in finite population sampling

Enrico Fabrizi, Parthasarathi Lahiri

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, various issues related to the implementation of the usual Bayesian Information Criterion (BIC) are critically examined in the context of modelling a finite population. A suitable design-based approximation to the BIC is proposed in order to avoid the derivation of the exact likelihood of the sample which is often very complex in a finite population sampling. The approximation is justified using a theoretical argument and a Monte Carlo simulation study
Original languageEnglish
Pages (from-to)289-301
Number of pages13
JournalSTATISTICA
VolumeLXXIII
DOIs
Publication statusPublished - 2013

Keywords

  • Bayes factor
  • Cluster sampling
  • Hypothesis testing
  • Model selection
  • Pseudo-maximum likelihood

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