Model selection for ecologists: the worldviews of AIC and BIC.

By Ken A. Aho, D. Derryberry, Teri Peterson. Ecology, 2014

1,102 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6ed116a04e77e56a49d482cb491c4c7e5ceb47d0

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In Ecology, Evolution, Behavior and Systematics

In Environmental Engineering

In Statistics and Probability

Citations in the corpus

Conversion of the chemical concentration of odorous mixtures into odour concentration and odour intensity: A comparison of methods (2015, neutral): “This means that this approach cannot be applied without olfactometric measurements; therefore it cannot be used universally (Akdeniz et al., 2012).”
Variable selection strategies and its importance in clinical prediction modelling (2020, neutral): “25 Depending on the situation, however, each criterion has an advantage over the other.”
Best practice guidance for linear mixed-effects models in psychological science (2019, background): “Aho et al. (2014) argue that AIC and BIC may be differently favoured in different inferential contexts (e.g., in their account, whether analyses are exploratory (AIC) or confirmatory (BIC)), and we highlight, for interested readers, a rich literature surrounding their use (e.g., Burnham & Anderson,…”
Sensitivity and Specificity of Information Criteria (2018, neutral): “Although the comparison of models is interpreted differently in the 226 theoretical frameworks used to justify AIC and BIC (Aho et al., 2014; Kuha, 2004), 227 algebraically this comparison is the same as a LRT (Söderström, 1977; Stoica et al.”
A global analysis of traits predicting species sensitivity to habitat fragmentation (2017, methodology): “We guarded against over-fitting by limiting model complexity to 12 terms at each step and comparing competing models using the Bayes information criterion (BIC; Burnham & Anderson, 2004; Muller et al., 2013; Aho et al., 2014).”
Model averaging and muddled multimodel inferences. (2015, background): “The initial enthusiasm for the informationtheoretic approach as an alternative paradigm for statistical inference is now being tempered by increased recognition of its direct relationship with other likelihood based statistics (Aho et al. 2014, Murtaugh 2014a, b, Spanos 2014).”
Carbon‐to‐chlorophyll ratio for phytoplankton in temperate coastal waters: Seasonal patterns and relationship to nutrients (2016, methodology): “AIC is the Akaike information criterion (Aho et al. 2014).”