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EnglishIn this article, various issues related to the implementation of the usual Bayesian Information Criterion (BIC) are critically examined in the context of modelling for finite populations. 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.
| Titolo: | A design-based approximation to the BIC in finite population sampling |
| Tutti gli autori: | FABRIZI, ENRICO; LAHIRI, PARTHA |
| Data di pubblicazione: | 2007 |
| Abstract (eng): | In this article, various issues related to the implementation of the usual Bayesian Information Criterion (BIC) are critically examined in the context of modelling for finite populations. 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. |
| Nelle collezioni: | Working papers at the Lorenzo Mascheroni Dep. of Mathematics, Statistics, Computing and Applications - Quaderni del Dip. di Matematica, statistica, informatica e applicazioni Lorenzo Mascheroni |
File allegato/i alla scheda:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| WPMateRi04(2007)FabriziLahiri.pdf | N/A | Open AccessVisualizza/Apri |
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