The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Repeated double cross validation
Abstract
Repeated double cross validation (rdCV) is a strategy for (a) optimizing the complexity of regression models and (b) for a realistic estimation of prediction errors when the model is applied to new cases (that are within the population of the data used). This strategy is suited for small data sets and is a complementary method to bootstrap methods. rdCV is a formal, partly new combination of known procedures and methods, and has been implemented in a function for the programming environment R, providing several types of plots for model evaluation. The current version of the software is dedicated to regression models obtained by partial least‐squares (PLS). The applied methods for repeated splits of the data into test sets and calibration sets, as well as for estimation of the optimum number of PLS components, are described. The relevance of some parameters (number of segments in CV, number of repetitions) is investigated. rdCV is applied to two data sets from chemistry: (1) determination of glucose concentrations from near infrared (NIR) data in mash samples from bioethanol production; (2) modeling the gas chromatographic retention indices of polycyclic aromatic compounds from molecular descriptors. Models using all original variables and models using a small subset of the variables, selected by a genetic algorithm (GA), are compared by rdCV. Copyright © 2009 John Wiley & Sons, Ltd.
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Cites
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
Cited by
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966
Translational biomarker discovery in clinical metabolomics: an introductory tutorial
Xia, J., Broadhurst, D. I., Wilson, M., & Wishart, D. S. (2013) · Metabolomics · doi:10.1007/s11306-012-0482-9 · Cited by 953
Multivariate data analysis in pharmaceutics: A tutorial review
Rajalahti, T., & Kvalheim, O. M. (2011) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2011.02.019 · Cited by 768
On-line application of near infrared (NIR) spectroscopy in food production
Porep, J. U., Kammerer, D. R., & Carle, R. (2015) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2015.10.002 · Cited by 611
Koutsouleris, N., Davatzikos, C., Borgwardt, S., Gaser, C., Bottlender, R., Frodl, T., Falkai, P., Riecher-Rossler, A., Moller, H.-J., Reiser, M., et al. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbt142 · Cited by 448
Metadata
- Authors
- Filzmoser, P., Liebmann, B., & Varmuza, K.
- Venue
- Journal of Chemometrics
- Publisher
- Wiley
- Published
- 2009-02-11
- Volume
- 23
- Issue
- 4
- Pages
- 160-171
- ISSN
- 0886-9383 (ISSN-L)
- 1099-128X
- OpenAlex
- W2090790364
- Cited by
- 483
- References
- 3
- FWCI
- 14.51
- Type
- journal article
- Language
- en
- Open access
- closed
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