The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR
Open accessAbstract
SummaryVariable selection can be challenging, particularly in situations with a large number of predictors with possibly high correlations, such as gene expression data. In this article, a new method called the OSCAR (octagonal shrinkage and clustering algorithm for regression) is proposed to simultaneously select variables while grouping them into predictive clusters. In addition to improving prediction accuracy and interpretation, these resulting groups can then be investigated further to discover what contributes to the group having a similar behavior. The technique is based on penalized least squares with a geometrically intuitive penalty function that shrinks some coefficients to exactly zero. Additionally, this penalty yields exact equality of some coefficients, encouraging correlated predictors that have a similar effect on the response to form predictive clusters represented by a single coefficient. The proposed procedure is shown to compare favorably to the existing shrinkage and variable selection techniques in terms of both prediction error and model complexity, while yielding the additional grouping information.
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Cites
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Regularization and Variable Selection Via the Elastic Net
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Model Selection and Estimation in Regression with Grouped Variables
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Sparsity and Smoothness Via the Fused Lasso
Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., & Knight, K. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00490.x · Cited by 2,724
A Multivariate Exponential Distribution
Marshall, A. W., & Olkin, I. (1967) · Journal of the American Statistical Association · doi:10.1080/01621459.1967.10482885 · Cited by 1,582
On the “degrees of freedom” of the lasso
Zou, H., Hastie, T., & Tibshirani, R. (2007) · The Annals of Statistics · doi:10.1214/009053607000000127 · Cited by 947
Ridge Regression: Biased Estimation for Nonorthogonal Problems
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Cited by
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Marquéz, J. R. G., Gruber, B., Lafourcade, B., Leitão, P. J., et al. (2013) · Ecography · doi:10.1111/j.1600-0587.2012.07348.x · Cited by 9,998
Permutation Tests for Studying Classifier Performance
Ojala, M., & Garriga, G. C. (2009) · 2009 Ninth IEEE International Conference on Data Mining · doi:10.1109/icdm.2009.108 · Cited by 555
SLOPE—Adaptive variable selection via convex optimization
Bogdan, M., van den Berg, E., Sabatti, C., Su, W., & Candès, E. J. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas842 · Cited by 277
Biclustering via Sparse Singular Value Decomposition
Lee, M., Shen, H., Huang, J. Z., & Marron, J. S. (2010) · Biometrics · doi:10.1111/j.1541-0420.2010.01392.x · Cited by 260
Metadata
- Authors
- Bondell, H. D., & Reich, B. J.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-02-26
- Volume
- 64
- Issue
- 1
- Pages
- 115-123
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2012653948
- Cited by
- 465
- References
- 15
- FWCI
- 10.51
- Type
- journal article
- Language
- en
- Open access
- green
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