Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.)

By M. Peruggia. 2003

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Measuring Effective Teacher-Student Interactions From a Student Perspective (2015, methodology): “The AIC was lower by 108.453 in the three-factor model, which indicates that the one-factor model is a likely worse fit than the three-factor model (Burnham & Anderson, 2002).”
The macroecology of phylogenetically structured hummingbird–plant networks (2015, methodology): “MAMs were defined as models exhibiting a difference in AICC of at least two points lower than other models (i.e. ΔAICC < 2; Burnham & Anderson, 2002; Diniz-Filho et al., 2008).”
Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors (2011, methodology): “When Δ AIC c Ͻ 4 (relative likelihood Ͻ 0.135), we evaluated parameter estimates by model averaging, also computing predictor weights (Burnham and Anderson 2002).”
A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection (2017, methodology): “Because basing inter - pretations on multiple models simultaneously is not straightforward, it is often recommended to average variable parameter estimates over models in the set; a method called model-averaging (Burnham & Anderson, 2002).”