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.
FABRIZI, Enrico, LAHIRI, PARTHA, (2007). A design-based approximation to the BIC in finite population sampling 4(2007)). Bergamo: Retrieved from http://hdl.handle.net/10446/330
A design-based approximation to the BIC in finite population sampling
FABRIZI, Enrico;
2007-01-01
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 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.| File | Dimensione del file | Formato | |
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