The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The Bayesian Lasso
Abstract
The Lasso estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters have independent Laplace (i.e., double-exponential) priors. Gibbs sampling from this posterior is possible using an expanded hierarchy with conjugate normal priors for the regression parameters and independent exponential priors on their variances. A connection with the inverse-Gaussian distribution provides tractable full conditional distributions. The Bayesian Lasso provides interval estimates (Bayesian credible intervals) that can guide variable selection. Moreover, the structure of the hierarchical model provides both Bayesian and likelihood methods for selecting the Lasso parameter. Slight modifications lead to Bayesian versions of other Lasso-related estimation methods, including bridge regression and a robust variant.
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Citations
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
Variable Selection via Gibbs Sampling
George, E. I., & McCulloch, R. E. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476353 · Cited by 2,828
Cited by
The horseshoe estimator for sparse signals
Carvalho, C. M., Polson, N. G., & Scott, J. G. (2010) · Biometrika · doi:10.1093/biomet/asq017 · Cited by 1,381
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H. (2017) · Statistical Science · doi:10.1214/16-sts576 · Cited by 1,085
Functional connectomics from resting-state fMRI
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952
Ciucci, F., & Chen, C. (2015) · Electrochimica Acta · doi:10.1016/j.electacta.2015.03.123 · Cited by 678
Penalized regression, standard errors, and Bayesian lassos
Casella, G., Ghosh, M., Gill, J., & Kyung, M. (2010) · Bayesian Analysis · doi:10.1214/10-ba607 · Cited by 567
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Metadata
- Authors
- Park, T., & Casella, G.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2008-06-01
- Volume
- 103
- Issue
- 482
- Pages
- 681-686
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1982652137
- Cited by
- 2,934
- References
- 3
- FWCI
- 64.88
- Type
- journal article
- Language
- en
- Open access
- closed
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