AIC and BIC

By Jouni Kuha. 2004

678 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0075219b4f331fab3bb09c472a46fe59103f4092

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Citations in the corpus

The costs of family and friends: an 18-month longitudinal study of relationship maintenance and decay (2011, methodology): “The BIC compares the goodness of fit of the models, whilst correcting for the number of parameters used (Kuha, 2004).”
Integrating Moderation and Mediation (2016, background): “Infinite dimensionality is not an unreasonable assumption as it implies that the processes that generate the data are rarely represented exactly by a fixed finite dimensional model (Kuha, 2004).”
Risk Classification for Claim Counts (2007, background): “Many other penalized criteria have been proposed in the statistical literature (see Kuha 2004 for an overview).”
The excess-zero problem in soil animal count data and choice of appropriate models for statistical inference (2008, methodology): “A more appropriate approach for comparing non-nested models is the use of information measures such as Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) (Johnson and Omland, 2004; Kuha, 2004).”
Goodness of Fit of Social Network Models (2008, neutral): “between AIC and the Bayesian information criterion (BIC); see, for example, Kuha (2004) .”
Extraverts Have Larger Social Network Layers But Do Not Feel Emotionally Closer to Individuals at Any Layer (2011, methodology): “We first fitted a model containing only extraversion, and if a significant effect of extraversion was found, we then proceeded sequentially by adding control variables until the AIC could no longer be lowered (we also examined the Bayesian information criterion BIC; Kuha, 2004) by use of the GENLIN ”
Sensitivity and Specificity of Information Criteria (2018, positive): “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.”
Drivers and barriers of eco-innovation types for sustainable transitions: A quantitative perspective (2018, background): “This is acceptable because of the different objectives of such criteria: Although the Bayesian information criterion's objective is to show the “true model,” the AIC aims to find the “better prediction” (Kuha, 2004).”
Comparison of Akaike information criterion (AIC) and Bayesian information criterion (BIC) in selection of an asymmetric price relationship (2010, methodology): “Although AIC and BIC have been compared theoretically and empirically (Weakliem, 1999; Kuha, 2004) and examined empirically with respect to selection of stock-recruitment relationships (Wang and Liu, 2006) there has been no”
Supervisor Prosocial Motivation, Employee Thriving, and Helping Behavior: A Trickle-Down Model of Psychological Safety (2018, methodology): “To assess the best fitting non-nested model, we report both AIC and BIC and use both together to assess model fit (Kuha, 2004).”