The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
The horseshoe estimator for sparse signals
Abstract
This paper proposes a new approach to sparsity, called the horseshoe estimator, which arises from a prior based on multivariate-normal scale mixtures. We describe the estimator’s advantages over existing approaches, including its robustness, adaptivity to different sparsity patterns and analytical tractability. We prove two theorems: one that characterizes the horseshoe estimator’s tail robustness and the other that demonstrates a super-efficient rate of convergence to the correct estimate of the sampling density in sparse situations. Finally, using both real and simulated data, we show that the horseshoe estimator corresponds quite closely to the answers obtained by Bayesian model averaging under a point-mass mixture prior.
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brms : An R Package for Bayesian Multilevel Models Using Stan
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“For more details see Carvalho et al. (2009, 2010).”
BackgroundMethodPolygenic prediction via Bayesian regression and continuous shrinkage priors
Ge, T., Chen, C.-Y., Ni, Y., Feng, Y.-C. A., & Smoller, J. W. (2019) · Nature Communications · doi:10.1038/s41467-019-09718-5 · Cited by 1,922
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
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Bayesian Compression for Deep Learning
Louizos, C., Ullrich, K., & Welling, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08665 · Cited by 312
Bayesian linear regression with sparse priors
Castillo, I., Schmidt-Hieber, J., & van der Vaart, A. (2015) · The Annals of Statistics · doi:10.1214/15-aos1334 · Cited by 277
Metadata
- Authors
- Carvalho, C. M., Polson, N. G., & Scott, J. G.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-04-28
- Volume
- 97
- Issue
- 2
- Pages
- 465-480
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2114169935
- Cited by
- 1,381
- References
- 44
- FWCI
- 26.12
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
