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Home Multivariate Data Modeling Validation of Models Validation of Models  
See also: PRESS, chance correlation, crossvalidation  
Validation of Models
Some (linear) multivariate methods provide theoretical foundation on the estimation of the reliability of such a model. When it comes to more sophisticated methods, or to nonlinear methods, the resulting models have to be validated by a heuristic approach. In principle, there are several methods to perform this, certain ones often being tailored to a specific model. One approach for validation, however, always performs quite well. This approach is called crossvalidation, also known as the "leaveoneout" method. Crossvalidation permits the determination of a measure for the prediction error called PRESS (prediction error sum of squares). Another little used procedure for the validation of models is the addition of noise and checking the reaction of the model.


Home Multivariate Data Modeling Validation of Models Validation of Models 
Last Update: 20121008