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Resumen de New insights into evaluation of regression models through a decomposition of the prediction errors: application to near-infrared spectral data

María Isabel Sánchez Rodríguez Árbol académico, Elena M. Sánchez López Árbol académico, José María Caridad y Ocerín Árbol académico, Alberto Marinas Aramendia, José María Marinas Rubio Árbol académico, Francisco José Urbano Navarro

  • This paper analyzes the performance of linear regression mo dels taking into account usual criteria such as the number of principal components or latent factors , the goodness of fit or the predictive capability. Other comparison criteria, more common in an ec onomic context, are also considered:

    the degree of multicollinearity and a decomposition of the m ean squared error of the prediction which determines the nature, systematic or random, of the pr ediction errors. The applications use real data of extra-virgin oil obtained by near-infrared spe ctroscopy. The high dimensionality of the data is reduced by applying principal component analysi s and partial least squares analysis.

    A possible improvement of these methods by using cluster ana lysis or the information of the relative maxima of the spectrum is investigated. Finally, o btained results are generalized via cross- validation and bootstrapping.


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