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

Enrico Fabrizi () and Parthasarathi Lahiri ()
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Enrico Fabrizi: Università Cattolica del Sacro Cuore, Piacenza - Iyaly
Parthasarathi Lahiri: University of Maryland, College Park, MD - U.S.A.

Statistica, 2013, vol. 73, issue 3, 289-301

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.

Keywords: Bayes factor; Hypothesis testing; Model selection; Pseudo-maximumlikelihood; Cluster sampling (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:73:y:2013:i:3:p:289-301

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