To evaluate model fit, common information criteria such as the Akaike's Information Criterion (AIC) and the Bayesian Information Criterion (BIC) were taken into account.
Note: AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion.
AIC = Akaike's Information Criterion; BIC = Bayes' Information Criterion.
Two common approaches in the literature include Akaike information criterion (AIC) [ 45] and Bayesian information criterion (BIC) [ 46].
Many methods for dealing with this problem have been investigated over the years, but the most common ones for order selection are the Akaike information criterion (AIC) [39] and the minimum description length criterion (MDL) [40] (see also [41]).
The common approach in selecting among such approaches is to use the Akaike information criterion (AIC).
CA, common ancestry; IA, independent ancestry; LLR, log-likelihood ratio; K, number of parameters in model; AIC, Akaike information criterion.
The common method of model selection makes use of information criteria based on the log likelihood, e.g. the Akaike information criterion (AIC) or the Bayesian information criterion (BIC) [ 13].
Common fit indexes are Chi-square, the significance of χ, the ratio of χ to degrees of freedom, Akaike information criterion (AIC), Bayes information criterion (BIC), Tucker Lewis index (TLI), Comparative fit index (CFI), root mean square error of approximation (RMSEA with 95%CI), standardised root mean square residual (SRMR) and the weighted root mean residual (WRMR) [ 19, 21].
Common model selection methods include likelihood ratio tests for nested models and, more generally, information theoretic measures, such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC) (Burnham and Anderson 2002; Sullivan and Joyce 2005).
