Application of Information Complexity in Principal Component Regression Modeling of the Venturi Meter Drift
نویسندگان
چکیده
In principal component regression there is a problem of selecting the number of principal components to be retained in the model. Those principal components corresponding to near-zero eigenvalues can ruin the precision of the regression coefficients estimator and therefore must be eliminated from the model. However, when the eigenspectrum gradually decays, it is difficult to decide how many principal components should be retained. In such situations cross-validation is usually used. This paper suggests another way of choosing the number of principal components by using the information complexity criterion ICOMP developed by Bozdogan for model selection (1988). An example of choosing the number of principal components to be retained in the model that predicts the venturi meter drift is given. The data for the example is from Florida Power Corporation's Crystal River Nuclear Power Plant.
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