New insights into evaluation of regression models through a decomposition of the prediction errors: application to near-infrared spectral data
- Autores: María Isabel Sánchez Rodríguez, Elena M. Sánchez López, José María Caridad y Ocerín, Alberto Marinas Aramendia, José María Marinas Rubio, Francisco José Urbano Navarro
- Localización: Sort: Statistics and Operations Research Transactions, ISSN 1696-2281, Vol. 37, Nº. 1, 2013, págs. 57-78
- Idioma: inglés
- Enlaces
- Dialnet Métricas: 2 Citas
-
Resumen
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.
