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

Author

Listed:
  • Enrico Fabrizi

    (Università Cattolica del Sacro Cuore, Piacenza - Iyaly)

  • Parthasarathi Lahiri

    (University of Maryland, College Park, MD - U.S.A.)

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.

Suggested Citation

  • Enrico Fabrizi & Parthasarathi Lahiri, 2013. "A design-based approximation to the Bayes Information Criterion in finite population sampling," Statistica, Department of Statistics, University of Bologna, vol. 73(3), pages 289-301.
    • Handle: RePEc:bot:rivsta:v:73:y:2013:i:3:p:289-301
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