The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Prediction by Supervised Principal Components
Abstract
In regression problems where the number of predictors greatly exceeds the number of observations, conventional regression techniques may produce unsatisfactory results. We describe a technique called supervised principal components that can be applied to this type of problem. Supervised principal components is similar to conventional principal components analysis except that it uses a subset of the predictors selected based on their association with the outcome. Supervised principal components can be applied to regression and generalized regression problems, such as survival analysis. It compares favorably to other techniques for this type of problem, and can also account for the effects of other covariates and help identify which predictor variables are most important. We also provide asymptotic consistency results to help support our empirical findings. These methods could become important tools for DNA microarray data, where they may be used to more accurately diagnose and treat cancer.
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Citations
Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Self-citationThe Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Self-citationGene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Sparse Principal Component Analysis
Zou, H., Hastie, T., & Tibshirani, R. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x113430 · Cited by 3,152
Self-citationKhan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., et al. (2001) · Nature Medicine · doi:10.1038/89044 · Cited by 2,644
Gene-expression profiles predict survival of patients with lung adenocarcinoma
Beer, D. G., Kardia, S. L. R., Huang, C.-C., Giordano, T. J., Levin, A. M., Misek, D. E., Lin, L., Chen, G., Gharib, T. G., Thomas, D. G., et al. (2002) · Nature Medicine · doi:10.1038/nm733 · Cited by 2,025
'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns
Hastie, T., Tibshirani, R., Eisen, M. B., Alizadeh, A., Levy, R., Staudt, L., Chan, W. C., Botstein, D., & Brown, P. (2000) · Genome Biology · doi:10.1186/gb-2000-1-2-research0003 · Cited by 547 · cited 2×
“Hastie et al. (2000) proposed “gene shaving” as a method for clustering genes.”
MethodSelf-citation
Cited by
Deletion of IKZF1 and Prognosis in Acute Lymphoblastic Leukemia
Mullighan, C. G., Su, X., Zhang, J., Radtke, I., Phillips, L. A. A., Miller, C. B., Ma, J., Liu, W., Cheng, C., Schulman, B. A., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808253 · Cited by 1,422
Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., & Huang, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101789 · Cited by 522
Enhancing Explainability of Neural Networks Through Architecture Constraints
Yang, Z., Zhang, A., & Sudjianto, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3007259 · Cited by 106
Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression
Chun, H., & Keleş, S. (2009) · Genetics · doi:10.1534/genetics.109.100362 · Cited by 97
Imputing Phenotypes for Genome-wide Association Studies
Hormozdiari, F., Kang, E. Y., Bilow, M., Ben-David, E., Vulpe, C., McLachlan, S., Lusis, A. J., Han, B., & Eskin, E. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.04.013 · Cited by 53
Metadata
- Authors
- Bair, E., Hastie, T., Paul, D., & Tibshirani, R.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2006-03
- Volume
- 101
- Issue
- 473
- Pages
- 119-137
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2019176983
- Cited by
- 817
- References
- 7
- FWCI
- 25.93
- Type
- journal article
- Language
- en
- Open access
- closed
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