Randomized trial evaluates model selection methods, highlighting AICc's superiority in small samples.
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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Bedrick et al. (1994) studied this question.
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