Is model selection using Akaike's information criterion appropriate for catch per unit effort standardization in large samples?

Abstract

Akaike’s information criterion (A/C), which is widely used as a criterion of model selection in fish population dynamics, is known to have a bias in not only small samples but also large samples. Consistency was proposed as a property of the information criteria available in large samples. We carried out model selection in ANOVA-type model corresponding to catch per unit effort (CPUE) standardization using consistent information criteria (Bayesian information criterion, Hannan-Quinn, or consistent A/C), which satisfy the asymptotic desirable property called consistency. The results of the model selections between these consistent criteria and A/C are different. Computer simulations using a linear regression model show that the selection performances of consistent information criteria in large samples are good compared with that of A/C.