Conventional likelihood-based information criteria for model selection rely on the distribution assumption of data. However, for complex data that are increasingly available in many scientific fields, the specification of their underlying distribution turns out to be challenging, and the existing criteria may be limited and are not general enough to handle a variety of model selection problems. We proposed a robust and consistent model selection criterion, named as ELCIC, based upon the empirical likelihood function which is data-driven. In particular, this framework adopts plug-in estimators that can be achieved by solving external estimating equations, not limited to the empirical likelihood, which avoids potential computational convergence issues and allows versatile applications, such as generalized linear models, generalized estimating equations, penalized regressions, and so on. The formulation of our proposed criterion is initially derived from the asymptotic expansion of the marginal likelihood under the variable selection framework, but more importantly, the consistent model selection property is established under a general context.
ELCIC offers a robust model assessment and can be applied to address more complicated situations where existing methods fail to work.
Please cite the following publication: Chixiang Chen, Ming Wang, Rongling Wu, and Runze, Li, A Robust Consistent Information Criterion for Model Selection based on Empirical Likelihood https://arxiv.org/pdf/2006.13281.pdf
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("chencxxy28/ELCIC")Please visit Tutorial