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 , Elena M. Sánchez López , José María Caridad y Ocerín , Alberto Marinas Aramendia, José María Marinas Rubio , 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.