We develop a small-sample criterion (AIC(C)) for selecting multivariate regression models. This criterion adjusts the Akaike information criterion to be an exact unbiased estimator for the expected Kullback-Leibler information. A small-sample comparison shows that AIC(C) provides better model order choices than other available model selection methods. Data from an agricultural experiment are analyzed.
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics