AIC model selection using Akaike weights

By Eric-Jan Wagenmakers, Simon Farrell. Psychonomic Bulletin & Review, 2004

2,501 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/80821ec147f413b530babd55f04b5f39ff34aba0

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Citations in the corpus

Spatial patterns and climate relationships of major plant traits in the New World differ between woody and herbaceous species (2018, methodology): “Next, we searched for climatic predictors of trait means and variances using model selection according to the Akaike information criteria (AIC) weight (Burnham & Anderson, 2002; Wagenmakers & Farrell, 2004).”
Estimation and interpretation of 1/fα noise in human cognition (2004, background): “Furthermore, differences in AIC values can be interpreted as strength of evidence (Wagenmakers & Farrell, 2004) and, most importantly, Monte Carlo simulations reported below show that correct model recovery based on AIC is better than that based on LRT.”
The influence of puberty on subcortical brain development (2014, neutral): “The heritability of brain structure and development is still not well-understood, but there are likely to be both genetic and environmental effects on structural brain volume and developmental trajectory, as well as pubertal timing, which are not independent between family members, and therefore…”
Length–mass allometry in snakes (2013, methodology): “We adjusted the strength of phylogenetic nonindependence using the maximum likelihood value of the scaling parameter l (Pagel, 1999) implemented in the R package CAIC (Orme, online, http://r-forge.r-project.org/projects/caic/).”
Reconciling intuitive physics and Newtonian mechanics for colliding objects. (2013, neutral): “For both measures, smaller values are better, and a measure of the relative posterior probability of the models assuming equal prior probabilities can be computed from a transformation of their AIC values (Akaike, 1978; Burnham & Anderson, 2002; Wagenmakers & Farrell, 2004).”
Prior sensitivity in theory testing: An apologia for the Bayes factor (2010, background): “…Accounted For (PVAF), the Maximum Likelihood Criterion (MLC: e.g., Myung, 2003) and its nephews the Akaike Information Criterion (AIC: Akaike, 1973; Wagenmakers & Farrell, 2004) and the Bayesian Information Criterion (BIC: Schwarz, 1978; Wagenmakers, 2007), consider the model’s prediction at a…”
Patterns of Attachment and Emotional Competence in Middle Childhood (2011, neutral): “To take account of model selection uncertainty, the statistic wi (‘Akaike weight’) can be computed (see Wagenmakers & Farrell, 2004).”
Ten simple rules for the computational modeling of behavioral data (2019, methodology): “For this reason, we focus primarily on the simplest modeling techniques most accessible to beginning modelers, but almost all of our points apply more generally and readers interested in more advanced modeling techniques should consult the many excellent tutorials, didactic examples, and books on th”