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The Adaptive Lasso and Its Oracle Properties
Abstract
The lasso is a popular technique for simultaneous estimation and variable selection. Lasso variable selection has been shown to be consistent under certain conditions. In this work we derive a necessary condition for the lasso variable selection to be consistent. Consequently, there exist certain scenarios where the lasso is inconsistent for variable selection. We then propose a new version of the lasso, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the ℓ1 penalty. We show that the adaptive lasso enjoys the oracle properties; namely, it performs as well as if the true underlying model were given in advance. Similar to the lasso, the adaptive lasso is shown to be near-minimax optimal. Furthermore, the adaptive lasso can be solved by the same efficient algorithm for solving the lasso. We also discuss the extension of the adaptive lasso in generalized linear models and show that the oracle properties still hold under mild regularity conditions. As a byproduct of our theory, the nonnegative garotte is shown to be consistent for variable selection.
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“Similarly, multivariate LASSO models (Zou, 2006;Nicolai and Bühlmann Peter, 2010;Mankoo et al., 2011) have been investigated.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zou, 2006
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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
(2005) · Springer eBooks · doi:10.1007/0-387-27255-0_9 · Cited by 20,333
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) · The Annals of Statistics · doi:10.1214/009053604000000067 · Cited by 9,357
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
B, J. F. A., & Li, R. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382273 · Cited by 8,919
Ideal Spatial Adaptation by Wavelet Shrinkage
Donoho, D. L., & Johnstone, I. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.425 · Cited by 8,730
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,030
Ziegel, E. R., Lehmann, E. L., & Casella, G. (1999) · Technometrics · doi:10.2307/1270597 · Cited by 4,203
Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ 1 minimization
Donoho, D. L., & Elad, M. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0437847100 · Cited by 2,970
Estimation of the Mean of a Multivariate Normal Distribution
Stein, C. M. (1981) · The Annals of Statistics · doi:10.1214/aos/1176345632 · Cited by 2,795
On Model Selection Consistency of Lasso
Zhao, P., & Yu, B. (2006) · doi:10.5555/1248547.1248637 · Cited by 2,314
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- Ser. B, 58, 267
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Climate policies that achieved major emission reductions: Global evidence from two decades
Stechemesser, A., Koch, N., Mark, E., Dilger, E., Klösel, P., Menicacci, L., Nachtigall, D., Pretis, F., Ritter, N., Schwarz, M., et al. (2024) · Science · doi:10.1126/science.adl6547 · Cited by 397
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Metadata
- Authors
- Zou, H.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- Zou, Hui
- ORCID
- Zou, Hui
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2006-12-01
- Volume
- 101
- Issue
- 476
- Pages
- 1418-1429
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2020925091
- Cited by
- 7,470
- References
- 23
- Citations by year
- 2026: 324 · 2025: 476 · 2024: 474 · 2023: 454 · 2022: 567 · 2021: 639 · 2020: 528 · 2019: 548 · 2018: 473 · 2017: 443 · 2016: 416 · 2015: 388 · 2014: 385 · 2013: 361 · 2012: 304
- FWCI
- 76.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Signal Processing › Sparse and Compressive Sensing Techniques
- Keywords
- adaptive LASSO, LASSO, feature selection, oracle property, consistency, adaptive weighting, l1 penalty, generalized linear models
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