Get access

MODEL SELECTION CRITERIA FOR LOGLINEAR MODELS

Authors


  • Acknowledgments. We thank the editorial staff and referees for many suggestions that improved the manuscript.

Author to whom correspondence should be addressed.

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.

Get access to the full text of this article

Ancillary