Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data

@inproceedings{Gao2010CompositeLB,
  title={Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data},
  author={Xin Gao and Peter X.-K. Song},
  year={2010}
}
For high-dimensional data sets with complicated dependency structures, the full likelihood approach often leads to intractable computational complexity. This imposes difficulty on model selection, given that most traditionally used information criteria require evaluation of the full likelihood. We propose a composite likelihood version of the Bayes information criterion (BIC) and establish its consistency property for the selection of the true underlying marginal model. Our proposed BIC is… CONTINUE READING

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