AIC-TYPE MODEL SELECTION CRITERION FOR MULTIVARIATE LINEAR REGRESSION WITH A FUTURE EXPERIMENT
This paper deals with the situation in which a current experiment is given and although a future design matrix has been prepared, the corresponding observation matrix is not available. To predict the future observation matrix, we consider selecting an appropriate design matrix by proposing a predictive Akaike Information Criteria (<i>PAIC</i>). The <i>PAIC</i> is derived as an exact unbiased estimator for the risk function and is based on the expected Kullback-Leibler divergence and the future design matrix. A simulation study illustrated that model selection with <i>PAIC</i> performs well for some extrapolation cases.
- JOURNAL OF THE JAPAN STATISTICAL SOCIETY
JOURNAL OF THE JAPAN STATISTICAL SOCIETY 27(2), 135-140, 1997
THE JAPAN STATISTICAL SOCIETY