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

Enrico Fabrizi, Parthasarathi Lahiri

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer 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
Lingua originaleEnglish
pagine (da-a)289-301
Numero di pagine13
RivistaSTATISTICA
VolumeLXXIII
DOI
Stato di pubblicazionePubblicato - 2013

Keywords

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

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