Model selection for multivariate regression in small samples
EJ Bedrick, CL Tsai - Biometrics, 1994 - JSTOR
Biometrics, 1994•JSTOR
We develop a small-sample criterion (AICC) 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 AICC
provides better model order choices than other available model selection methods. Data
from an agricultural experiment are analyzed.
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 AICC
provides better model order choices than other available model selection methods. Data
from an agricultural experiment are analyzed.
We develop a small-sample criterion (AICC) 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 AICC provides better model order choices than other available model selection methods. Data from an agricultural experiment are analyzed.
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