Information and Likelihood Theory: A Basis for Model Selection and Inference
By K. Burnham, David R. Anderson. 2004
348 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/3ac59d45176fffb0f71a63efc4ceeee9d400a2ca
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In Artificial Intelligence
Citations in the corpus
- Raising the flag on marine alien fouling species (2017, methodology): “Models were run for the full suite of variables using the Dredge function and the best model was selected based on Akaike criteria (Burnham and Anderson 2002).”
- Bats as potential suppressors of multiple agricultural pests: A case study from Madagascar (2019, methodology): “…data, negative binomial or Poisson generalised linear mixed models (GLMMs) with a log link function were used to model the relationship between bat activity and landcover type (continuous forest, forest fragments, secondary vegetation, hillside rice and irrigated rice) (Burnham and Anderson, 2003).”
- Environmental DNA surveillance for invertebrate species: advantages and technical limitations to detect invasive crayfish Procambarus clarkii in freshwater ponds. (2014, methodology): “AICc weights (wAICc), that is an estimate of the probability that the focal model is chosen as being the best fit model if the study is repeated (Burnham & Anderson 2002), were also calculated for each model.”