The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The solution path of the generalized lasso
Open accessAbstract
We present a path algorithm for the generalized lasso problem. This problem penalizes the ℓ1 norm of a matrix D times the coefficient vector, and has a wide range of applications, dictated by the choice of D. Our algorithm is based on solving the dual of the generalized lasso, which greatly facilitates computation of the path. For D = I (the usual lasso), we draw a connection between our approach and the well-known LARS algorithm. For an arbitrary D, we derive an unbiased estimate of the degrees of freedom of the generalized lasso fit. This estimate turns out to be quite intuitive in many applications.
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Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
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-citation
Cited by
Multiple predicting K -fold cross-validation for model selection
Jung, Y. (2018) · Journal of Nonparametric Statistics · doi:10.1080/10485252.2017.1404598 · Cited by 499
Metadata
- Authors
- Tibshirani, R. J., & Taylor, J.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2011-06-01
- Volume
- 39
- Issue
- 3
- Pages
- 1335-1371
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W1977777127
- Cited by
- 553
- References
- 2
- FWCI
- 13.63
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Tibshirani, R. J., & Taylor, J. (2011). The solution path of the generalized lasso. The Annals of Statistics. https://doi.org/10.1214/11-aos878
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