The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
A review of Bayesian variable selection methods: what, how and which
Open accessAbstract
The selection of variables in regression problems has occupied the minds of many statisticians. Several Bayesian variable selection methods have been developed, and we concentrate on the following methods: Kuo & Mallick, Gibbs Variable Selection (GVS), Stochastic Search Variable Selection (SSVS), adaptive shrinkage with Jeffreys' prior or a Laplacian prior, and reversible jump MCMC. We review these methods, in the context of their different properties. We then implement the methods in BUGS, using both real and simulated data as examples, and investigate how the different methods perform in practice. Our results suggest that SSVS, reversible jump MCMC and adaptive shrinkage methods can all work well, but the choice of which method is better will depend on the priors that are used, and also on how they are implemented.
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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
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
Meuwissen, T. H. E., Hayes, B. J., & Goddard, M. E. (2001) · Genetics · doi:10.1093/genetics/157.4.1819 · Cited by 7,917
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
GREEN, P. J. (1995) · Biometrika · doi:10.1093/biomet/82.4.711 · Cited by 5,930
Monte Carlo Statistical Methods
Kim, H. (2000) · Technometrics · doi:10.1080/00401706.2000.10485722 · Cited by 5,508
Monte Carlo Statistical Methods
Robert, C. P., & Casella, G. (2004) · Springer Texts in Statistics · doi:10.1007/978-1-4757-4145-2 · Cited by 4,271
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050 · cited 3×
“σβ ∼ U(0, 20), (5) where U(a, b) denotes a uniform distribution between a and b (for a justification of this prior, see Gelman (2006)).”
BackgroundMethodInfluentialPark, T., & Casella, G. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000337 · Cited by 2,934
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
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A simple new approach to variable selection in regression, with application to genetic fine mapping
Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12388 · Cited by 1,121
A guide to Bayesian model selection for ecologists
Hooten, M. B., & Hobbs, N. T. (2015) · Ecological Monographs · doi:10.1890/14-0661.1 · Cited by 891
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Ovaskainen, O., & Abrego, N. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108591720 · Cited by 394
Bayesian LASSO for Quantitative Trait Loci Mapping
Yi, N., & Xu, S. (2008) · Genetics · doi:10.1534/genetics.107.085589 · Cited by 344
A multispecies occupancy model for two or more interacting species
Rota, C. T., Ferreira, M. A. R., Kays, R. W., Forrester, T. D., Kalies, E. L., McShea, W. J., Parsons, A. W., & Millspaugh, J. J. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12587 · Cited by 284
Metadata
- Authors
- O'Hara, R. B., & Sillanpää, M. J.
- Venue
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Published
- 2009-03-01
- Volume
- 4
- Issue
- 1
- Pages
- 85-117
- ISSN
- 1931-6690 (ISSN-L)
- 1936-0975
- OpenAlex
- W2049228615
- Cited by
- 771
- References
- 49
- FWCI
- 21.86
- Type
- journal article
- Language
- en
- Open access
- diamond
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