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) |
|---|---|
| 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
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics