Model selection for multivariate regression in small samples

E. J. Bedrick; C. L. Tsai

doi:10.2307/2533213

Model selection for multivariate regression in small samples

E. J. Bedrick, C. L. Tsai

Research output: Contribution to journal › Article › peer-review

164 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 language	English (US)
Pages (from-to)	226-231
Number of pages	6
Journal	Biometrics
Volume	50
Issue number	1
DOIs	https://doi.org/10.2307/2533213
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

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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.",

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AB - 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.

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Model selection for multivariate regression in small samples

Abstract

ASJC Scopus subject areas

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