The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Penalized regression, standard errors, and Bayesian lassos
Open accessAbstract
Penalized regression methods for simultaneous variable selection and coefficient estimation, especially those based on the lasso of Tibshirani (1996), have received a great deal of attention in recent years, mostly through frequentist models. Properties such as consistency have been studied, and are achieved by different lasso variations. Here we look at a fully Bayesian formulation of the problem, which is flexible enough to encompass most versions of the lasso that have been previously considered. The advantages of the hierarchical Bayesian formulations are many. In addition to the usual ease-of-interpretation of hierarchical models, the Bayesian formulation produces valid standard errors (which can be problematic for the frequentist lasso), and is based on a geometrically ergodic Markov chain. We compare the performance of the Bayesian lassos to their frequentist counterparts using simulations, data sets that previous lasso papers have used, and a difficult modeling problem for predicting the collapse of governments around the world. In terms of prediction mean squared error, the Bayesian lasso performance is similar to and, in some cases, better than, the frequentist lasso.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602
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
Fan, J., & Li, R. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501753382273 · Cited by 8,900
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470
Model Selection and Estimation in Regression with Grouped Variables
Yuan, M., & Lin, Y. (2006) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00532.x · Cited by 7,261
Markov Chains and Stochastic Stability
Meyn, S. P., & Tweedie, R. L. (1993) · Springer eBooks · doi:10.1007/978-1-4471-3267-7 · Cited by 5,041
Ridge Regression: Applications to Nonorthogonal Problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488635 · Cited by 2,956
Park, T., & Casella, G. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000337 · Cited by 2,934
Self-citation
Cited by
A practical guide to selecting models for exploration, inference, and prediction in ecology
Tredennick, A. T., Hooker, G., Ellner, S. P., & Adler, P. B. (2021) · Ecology · doi:10.1002/ecy.3336 · Cited by 474
Mathieu, J. A., Hatté, C., Balesdent, J., & Parent, É. (2015) · Global Change Biology · doi:10.1111/gcb.13012 · Cited by 291
Precinct or prejudice? Understanding racial disparities in New York City’s stop-and-frisk policy
Goel, S., Rao, J. M., & Shroff, R. (2016) · The Annals of Applied Statistics · doi:10.1214/15-aoas897 · Cited by 285
Chen, W., McDonnell, S. K., Thibodeau, S. N., Tillmans, L. S., & Schaid, D. J. (2016) · Genetics · doi:10.1534/genetics.116.188953 · Cited by 52
A Bayesian Alternative to Synthetic Control for Comparative Case Studies
Pang, X., Liu, L., & Xu, Y. (2022) · Political Analysis · doi:10.1017/pan.2021.22 · Cited by 57
Metadata
- Authors
- Casella, G., Ghosh, M., Gill, J., & Kyung, M.
- Venue
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Published
- 2010-06-01
- Volume
- 5
- Issue
- 2
- Pages
- 369-411
- ISSN
- 1931-6690 (ISSN-L)
- 1936-0975
- OpenAlex
- W1976365540
- Cited by
- 567
- References
- 34
- FWCI
- 20.74
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Penalized regression, standard errors, and Bayesian lassos. https://app.cochat.ai/papers/10.1214/10-ba607
- The paper
- Casella, G., Ghosh, M., Gill, J., & Kyung, M. (2010). Penalized regression, standard errors, and Bayesian lassos. Bayesian Analysis. https://doi.org/10.1214/10-ba607
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
