AICM

(redirected from Akaike's Information Criterion)
AcronymDefinition
AICMAeropuerto Internacional de la Ciudad de México (Spanish: Mexico City International Airport; Mexico City, Mexico)
AICMAkaike's Information Criterion (statistical models)
AICMAustralian Institute of Credit Management
AICMAdvances in Computational Mathematics (textbook)
AICMAnd I Claim My...
AICMAnti-Inflammatory Controller Medication
AICMArie and Ida Crown Memorial (Chicago, IL)
AICMAssociazione Italiana di Citogenetica Medica
AICMAssociation des Ingénieurs Commerciaux de Mons (French: Commercial Engineers Association of Mons; Mons, Belgium)
AICMAssociate of the Institute of Credit Management
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References in periodicals archive ?
We used the Akaike's Information Criterion with small-sample correction (AICc) to select the best models (i.e., [DELTA]AICc values < 2).
Iriobe, Obamuyi, & Abayomi (2018) explained that before the ARDL bound test is conducted, the next step of ARDL model estimation step is to determine the optimal lag length of each variable by using Akaike's Information Criterion (AIC) or Schwarz Bayesian Criterion (SBC).
The models were evaluated by the following criteria: coefficient of determination of model ([R.sup.2]), Akaike's information criterion (AIC), by sum of squared residuals (SSR) and by Willmott's index (d).
Length-at-age data were analyzed by using 9 growth models, and the best-fit-model was determined by using Akaike's information criterion (AIC).
According to regression results, we calculated the Akaike's Information Criterion (AIC) and Akaike's Information Criterion for small sample sizes (AICc) to choose the most parsimonious model that offered the highest accuracy with the least variables (Anderson and Burnham, 1999; Pan, 2001; Ong-In et al., 2016).
To choose the best models we used a modified version of Akaike's information criterion ([AIC.sub.c]) for small sample sizes (Burnham & Anderson 2002).
A brief guide to model selection, multimodel inference, and model averaging in behavioral ecology using Akaike's information criterion. Behavioral Ecology & Sociobiology 65:13-21.
We used Akaike's information criterion for small sample sizes ([AIC.sub.c]), changes in [AIC.sub.c] and [DELTA][AIC.sub.c] values, and Akaike weights (AIC[omega]) to evaluate model performance and select the best approximating model (Burnham and Anderson 2002).
Akaike's information criterion [20-22] (AIC) is a measure of the goodness of fit of an estimated statistical model and a tool for model selection.
The quality of fit obtained by the models was evaluated based on the following criteria: Adjusted coefficient of determination ([R.sup.a.sub.aj]), Akaike's information criterion (AIC), residual standard deviation (RSD) and Likelihood Ratio Test (LRT).
In order to evaluate growth functions, the R2 (coefficient of determination), adjusted R2 (adjusted coefficient of determination), MSE (mean square error), AIC (Akaike's information criterion) and BIC (Bayesian information criterion) goodness of fit criteria were used.
Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses.