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MODEL SELECTION CRITERIA FOR LOGLINEAR MODELS
Author(s) -
Bedrick Edward J.,
Crandall Winston K.
Publication year - 2010
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2010.00593.x
Subject(s) - log linear model , akaike information criterion , mathematics , model selection , statistics , poisson regression , selection (genetic algorithm) , econometrics , poisson distribution , information criteria , linear model , computer science , population , demography , sociology , artificial intelligence
Summary Considerable work has been devoted to developing model selection criteria for normal theory regression models. Less attention has been paid to discrete data. We develop two loglinear model selection criteria for Poisson counts. These criteria are based on an estimated bias adjustment of the Akaike information criterion. We observe in a simulation study that the corrected statistics provide good model choices and relatively accurate estimates of the mean structure.