• Title of article

    Dimension of linear models

  • Author/Authors

    Hِskuldsson، نويسنده , , Agnar، نويسنده ,

  • Issue Information
    دوفصلنامه با شماره پیاپی سال 1996
  • Pages
    19
  • From page
    37
  • To page
    55
  • Abstract
    Determination of the proper dimension of a given linear model is one of the most important tasks in the applied modeling work. We consider here eight criteria that can be used to determine the dimension of the model, or equivalently, the number of components to use in the model. Four of these criteria are widely used ones, while the remaining four are ones derived from the H-principle of mathematical modeling. Many examples from practice show that the criteria derived from the H-principle function better than the known and popular criteria for the number of components. We shall briefly review the basic problems in determining the dimension of linear models. Then each of the eight measures are treated. The results are illustrated by examples.
  • Keywords
    Mallows Cp , AIC , Cross Validation , Covariance , Leaving one out , bias , linear models , Prediction variance , number of components , Dimension , H-principle
  • Journal title
    Chemometrics and Intelligent Laboratory Systems
  • Serial Year
    1996
  • Journal title
    Chemometrics and Intelligent Laboratory Systems
  • Record number

    1459489