TITLE

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

AUTHOR(S)
Shono, Hiroshi
PUB. DATE
October 2005
SOURCE
Fisheries Science;Oct2005, Vol. 71 Issue 5, p978
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Akaike's information criterion ( AIC), 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 AIC), which satisfy the asymptotic desirable property called consistency. The results of the model selections between these consistent criteria and AIC 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 AIC.
ACCESSION #
18589955

 

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