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
Link To Document :
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