The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
L 1-Regularization Path Algorithm for Generalized Linear Models
Abstract
Summary: We introduce a path following algorithm for L1-regularized generalized linear models. The L1-regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L1-norm of the coefficients, in a manner that is less greedy than forward selection–backward deletion. The generalized linear model path algorithm efficiently computes solutions along the entire regularization path by using the predictor–corrector method of convex optimization. Selecting the step length of the regularization parameter is critical in controlling the overall accuracy of the paths; we suggest intuitive and flexible strategies for choosing appropriate values. We demonstrate the implementation with several simulated and real data sets.
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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
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
The 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-citationMcCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
A solution to the problem of separation in logistic regression
Heinze, G., & Schemper, M. (2002) · Statistics in Medicine · doi:10.1002/sim.1047 · Cited by 2,711
Cited by
Variable selection using random forests
Genuer, R., Poggi, J.-M., & Tuleau-Malot, C. (2010) · Pattern Recognition Letters · doi:10.1016/j.patrec.2010.03.014 · Cited by 2,619
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912
High-Dimensional Methods and Inference on Structural and Treatment Effects
Belloni, A., Chernozhukov, V., & Hansen, C. (2014) · Journal of Economic Perspectives · doi:10.1257/jep.28.2.29 · Cited by 722
A significance test for the lasso
Lockhart, R., Taylor, J., Tibshirani, R. J., & Tibshirani, R. (2014) · The Annals of Statistics · doi:10.1214/13-aos1175 · Cited by 541
Yao, J., Zhu, X., Jonnagaddala, J., Hawkins, N., & Huang, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101789 · Cited by 522
Metadata
- Authors
- Park, M. Y., & Hastie, T.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-08-07
- Volume
- 69
- Issue
- 4
- Pages
- 659-677
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2101095383
- Cited by
- 838
- References
- 5
- FWCI
- 55.56
- Type
- journal article
- Language
- en
- Open access
- closed
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