Model comparison with composite likelihood information criteria

Model comparison with composite likelihood information criteria

Comparisons are made for the amount of agreement of the composite likelihood information criteria and their full likelihood counterparts when making decisions among the fits of different models, and some properties of penalty term for composite likelihood information criteria are obtained. Asymptotic theory is given for the case when a simpler model is nested within a bigger model, and the bigg...

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

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 ...

Information Geometry and Maximum Likelihood Criteria

DRAFT An Information Theoretic Comparison of Model Selection Criteria

Information theory offers a coherent perspective on model selection. As in Rissanen’s original application of information theory to model selection, our perspective arises from viewing a model as a component of a compressed representation of data in a two-part code. The first part of such a code is an explicit representation of the model used to compress the data. Simpler models have shorter re...

Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation

Local likelihood estimation has proven to be an eeective method for obtaining estimates of parameters that vary with a covariate. To obtain useful estimates of such parameters, approximating models are used. In such cases it is useful to consider window based estimates. We may need to choose between competing approximating models. In this paper we propose a modiication to the methods used to mo...