Model selection for multivariate regression in small samples

E. J. Bedrick, C. L. Tsai

Research output: Contribution to journalArticlepeer-review

123 Scopus citations

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 languageEnglish (US)
Pages (from-to)226-231
Number of pages6
JournalBiometrics
Volume50
Issue number1
DOIs
StatePublished - 1994
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Model selection for multivariate regression in small samples'. Together they form a unique fingerprint.

Cite this