文章基本信息

标题：AIC-TYPE MODEL SELECTION CRITERION FOR MULTIVARIATE LINEAR REGRESSION WITH A FUTURE EXPERIMENT
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作者：Kenichi Satoh
期刊名称：JOURNAL OF THE JAPAN STATISTICAL SOCIETY
印刷版ISSN：1882-2754
电子版ISSN：1348-6365
出版年度：1997
卷号：27
期号：2
页码：135-140
DOI：10.14490/jjss1995.27.135
出版社：JAPAN STATISTICAL SOCIETY
摘要：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 ( PAIC ). The PAIC 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 PAIC performs well for some extrapolation cases.
关键词：AIC;exact unbiased;extrapolation;predictive AIC;multivariate linear regression