AIC-TYPE MODEL SELECTION CRITERION FOR MULTIVARIATE LINEAR REGRESSION WITH A FUTURE EXPERIMENT

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著者

    • Satoh Kenichi
    • Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University

抄録

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.

収録刊行物

  • JOURNAL OF THE JAPAN STATISTICAL SOCIETY

    JOURNAL OF THE JAPAN STATISTICAL SOCIETY 27(2), 135-140, 1997

    THE JAPAN STATISTICAL SOCIETY

各種コード

  • NII論文ID(NAID)
    130003582677
  • 本文言語コード
    EN
  • ISSN
    1882-2754
  • データ提供元
    J-STAGE 
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