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

• Satoh Kenichi

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

Abstract

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.

Journal

• JOURNAL OF THE JAPAN STATISTICAL SOCIETY

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

  THE JAPAN STATISTICAL SOCIETY

Keywords

• AIC
• exact unbiased
• extrapolation
• predictive AIC
• multivariate linear regression

Details

• CRID: 1390282680265097984
• NII Article ID: 130003582677
• DOI: 10.14490/jjss1995.27.135
• ISSN: 13486365
• ISSN: 18822754
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed