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线性混合效应模型中的模型平均与权重选择AI 解读

This article studies model averaging for linear mixed-effects models. We establish an unbiased estimator of the squared risk for the model averaging, and use the estimator as a criterion for choosing weights. The resulting model average estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments show it is superior or comparable to estimators based on the final models selected by the commonly-used methods and some existing…

线性混合效应模型条件 AIC 的推广AI 解读

The conventional model selection criterion AIC has been applied to choose candidate models in mixed-effects models by the consideration of marginal likelihood. Vaida and Blanchard (2005) demonstrated that such a marginal AIC and its small sample correction are inappropriate when the research focus is on clusters. Correspondingly, these authors suggested to use conditional AIC. The conditional AIC is derived under the assumptions of the variance-covariance matrix or scaled…