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A modification of the ICOMP criterion for estimation of optimum complexity of PCR models
Author(s) -
Capron X.,
Walczak B.,
de Noord O. E.,
Massart D. L.
Publication year - 2005
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.934
Subject(s) - cross validation , information criteria , computer science , variance (accounting) , latent variable , model selection , bayesian information criterion , regression , computation , statistics , data mining , bilinear interpolation , regression analysis , monte carlo method , mathematics , algorithm , accounting , business
The predictive ability of a PCR bilinear regression model is highly dependent on the number of latent variables selected. A non‐optimal complexity is likely to result in a model yielding unsatisfactory predictions, due to a high bias or high variance of the coefficients of regression. The popular cross‐validation methods such as leave‐one‐out cross‐validation (LOOCV) and Monte‐Carlo cross‐validation (MCCV) are not always able to retain the proper number of latent variables, especially when atypical samples are present in the data. Also, they are computationally intensive, particularly for large data sets. In this study, the information complexity criterion ICOMP is modified in order to select the optimal PCR model. The results obtained demonstrate that this information criterion behaves at least as good as the cross‐validation approaches, and usually outperforms them in terms of model selection and computation time, whether atypical samples are present in the data or not. Copyright © 2006 John Wiley & Sons, Ltd.