The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm
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Cites
FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY
Altman, E. I. (1968) · The Journal of Finance · doi:10.1111/j.1540-6261.1968.tb00843.x · Cited by 14,888
Gunst, R. F. (1999) · Technometrics · doi:10.1080/00401706.1999.10485680 · Cited by 4,002
Haaland, D. M., & Thomas, E. V. (1988) · Analytical Chemistry · doi:10.1021/ac00162a020 · Cited by 2,539
Performance of some variable selection methods when multicollinearity is present
Chong, I.-G., & Jun, C.-H. (2005) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.12.011 · Cited by 2,049
A user-friendly guide to Multivariate Calibration and Classification
Næs, T., Isaksson, T., Fearn, T., & Davies, T. (2017) · doi:10.1255/978-1-906715-25-0 · Cited by 1,401
The successive projections algorithm for variable selection in spectroscopic multicomponent analysis
Araújo, M. C. U., Saldanha, T. C. B., Galvão, R. K. H., Yoneyama, T., Chame, H. C., & Visani, V. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00119-8 · Cited by 1,320
Self-citationChemometrics: A Practical Guide
Tobias, R. D. (1999) · Technometrics · doi:10.1080/00401706.1999.10485949 · Cited by 1,308
A method for calibration and validation subset partitioning
Galvão, R. K. H., de Araújo, M. C. U., José, G. E., et al. (2005) · Talanta · doi:10.1016/j.talanta.2005.03.025 · Cited by 938
Self-citationA User-Friendly Guide to Multivariate Calibration and Classification
Ziegel, E. R. (2004) · Technometrics · doi:10.1198/004017004000000167 · Cited by 916
The successive projections algorithm for spectral variable selection in classification problems
Pontes, M. J. C., Galvão, R. K. H., Araújo, M. C. U., Moreira, P. N. T., Neto, O. D. P., José, G. E., & Saldanha, T. C. B. (2005) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2004.12.001 · Cited by 177
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Cited by
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Metadata
- Authors
- Galvão, R. K. H., Araújo, M. C. U., Fragoso, W. D., Silva, E. C., José, G. E., Soares, S. F. C., & Paiva, H. M.
- Venue
- Chemometrics and Intelligent Laboratory Systems
- Publisher
- Elsevier BV
- Published
- 2008-05
- Volume
- 92
- Issue
- 1
- Pages
- 83-91
- ISSN
- 0169-7439 (ISSN-L)
- OpenAlex
- W2040104179
- Cited by
- 255
- References
- 12
- FWCI
- 13.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. https://app.cochat.ai/papers/10.1016/j.chemolab.2007.12.004
- The paper
- Galvão, R. K. H., Araújo, M. C. U., Fragoso, W. D., Silva, E. C., José, G. E., Soares, S. F. C., & Paiva, H. M. (2008). A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2007.12.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
