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. Here, we propose a robust and consistent model selection criterion 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 variable selection framework, but more importantly, the consistent model selection property is established under a general context. Extensive simulation studies confirm the out-performance of the proposal compared to traditional model selection criteria. Finally, an application to the Atherosclerosis Risk in Communities Study illustrates the practical value of this proposed framework.

中文翻译：

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基于经验似然的模型选择的稳健一致信息准则

用于模型选择的传统基于似然的信息标准依赖于数据的分布假设。然而，对于在许多科学领域越来越可用的复杂数据，其潜在分布的规范证明是具有挑战性的，并且现有的标准可能是有限的，并且不足以处理各种模型选择问题。在这里，我们基于数据驱动的经验似然函数提出了一个稳健且一致的模型选择标准。特别是，该框架采用了可通过求解外部估计方程来实现的插件估计器，不限于经验似然，避免了潜在的计算收敛问题，并允许通用应用，如广义线性模型、广义估计方程、惩罚回归等。我们提出的标准的制定最初源自变量选择框架下边际似然的渐近扩展，但更重要的是，一致的模型选择属性是在一般背景下建立的。广泛的模拟研究证实了与传统的模型选择标准相比，该提案的表现优异。最后，社区研究中动脉粥样硬化风险的应用说明了该提议框架的实用价值。广泛的模拟研究证实，与传统的模型选择标准相比，该提案的表现优异。最后，社区研究中动脉粥样硬化风险的应用说明了该建议框架的实用价值。广泛的模拟研究证实，与传统的模型选择标准相比，该提案的表现优异。最后，社区研究中动脉粥样硬化风险的应用说明了该建议框架的实用价值。