The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Sparse Additive Models
Open accessAbstract
Summary: We present a new class of methods for high dimensional non-parametric regression and classification called sparse additive models. Our methods combine ideas from sparse linear modelling and additive non-parametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. Sparse additive models are essentially a functional version of the grouped lasso of Yuan and Lin. They are also closely related to the COSSO model of Lin and Zhang but decouple smoothing and sparsity, enabling the use of arbitrary non-parametric smoothers. We give an analysis of the theoretical properties of sparse additive models and present empirical results on synthetic and real data, showing that they can be effective in fitting sparse non-parametric models in high dimensional data.
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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
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
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Vaart, A. W. V. D. (1998) · doi:10.1017/cbo9780511802256 · Cited by 4,159
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Distribution-Free Predictive Inference for Regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1307116 · Cited by 1,049
Self-citationInterpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Optimization with Sparsity-Inducing Penalties
Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000015 · Cited by 730
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
Liu, H., Lafferty, J., & Wasserman, L. (2018) · doi:10.1184/r1/6610712.v1 · Cited by 621
Self-citationOptimization with Sparsity-Inducing Penalties
Bach, F. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601985118 · Cited by 608
Metadata
- Authors
- Ravikumar, P., Lafferty, J., Liu, H., & Wasserman, L.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-10-19
- Volume
- 71
- Issue
- 5
- Pages
- 1009-1030
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2593996946
- Cited by
- 535
- References
- 5
- FWCI
- 19.40
- Type
- journal article
- Language
- en
- Open access
- bronze
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