Abstract
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.
Original language | English (US) |
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Pages (from-to) | 226-231 |
Number of pages | 6 |
Journal | Biometrics |
Volume | 50 |
Issue number | 1 |
DOIs | |
State | Published - 1994 |
Externally published | Yes |
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics