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

2005 ◽  
Vol 71 (5) ◽  
pp. 978-986 ◽  
Author(s):  
Hiroshi SHONO
Keyword(s):  
Model Selection ◽  
Information Criterion ◽  
Catch Per Unit Effort ◽  
Akaike's Information Criterion ◽  
Akaike’S Information Criterion ◽  
Large Samples ◽  
Unit Effort

scholarly journals Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data

2016 ◽  
Vol 37 (2) ◽  
Author(s):  
A.H.M. Mahbub Latif ◽  
M. Zakir Hossain ◽  
M. Ataharul Islam
Keyword(s):  
Model Selection ◽  
Generalized Estimating Equations ◽  
Likelihood Function ◽  
Selection Criterion ◽  
Information Criterion ◽  
Binary Response ◽  
Akaike's Information Criterion ◽  
Akaike’S Information Criterion ◽  
Model Selection Criterion ◽  
Morbidity Data

The most commonly used model selection criterion, Akaike’s Information Criterion (AIC), cannot be used when the Generalized Estimating Equations (GEE) approach is considered for analyzing multivariate binary response. Recently, a modified version of AIC (mAIC) which is based on quasi-likelihood function is proposed as a model selection criterion. This model selection criterion can be used in the GEE setup. In this study, an application of mAIC is showed in selecting important covariates associated with pregnancy related complications of Bangladeshi women.

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