论文信息 - MODEL SELECTION FOR MULTIVARIATE REGRESSION IN SMALL SAMPLES

MODEL SELECTION FOR MULTIVARIATE REGRESSION 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.

Chih-Ling Tsai | E. Bedrick

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