The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Bayesian CART Model Search
Abstract
In this article we put forward a Bayesian approach for finding classification and regression tree (CART) models. The two basic components of this approach consist of prior specification and stochastic search. The basic idea is to have the prior induce a posterior distribution that will guide the stochastic search toward more promising CART models. As the search proceeds, such models can then be selected with a variety of criteria, such as posterior probability, marginal likelihood, residual sum of squares or misclassification rates. Examples are used to illustrate the potential superiority of this approach over alternative methods.
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Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,522
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 20,825
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
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
Markov Chains for Exploring Posterior Distributions
Tierney, L. (1994) · The Annals of Statistics · doi:10.1214/aos/1176325750 · Cited by 3,452
Hierarchical Mixtures of Experts and the EM Algorithm
Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,673
Mallows, C. L. (1995) · Technometrics · doi:10.1080/00401706.1995.10484370 · Cited by 2,083
Multisurface method of pattern separation for medical diagnosis applied to breast cytology.
Wolberg, W. H., & Mangasarian, O. L. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.23.9193 · Cited by 1,037
Inferring decision trees using the minimum description lenght principle
Ross Quinlan, J., & Rivest, R. L. (1989) · Information and Computation · doi:10.1016/0890-5401(89)90010-2 · Cited by 652
Clark, L. A., & Pregibon, D. (2017) · Statistical Models in S · doi:10.1201/9780203738535-9 · Cited by 560
Research works printed but not linked: 8
- Clark L., 1992, Statistical Models in S
- Chipman H. A., 1998, “Hierarchical Priors for Bayesian CART Shrinkage,”
- Denison D. G. T., Biometrika, 85
- George E. I., 1998, Encyclopedia of Statistical Sciences Update, 3
- Oliver J. J., Proceedings of the International Machine Learning Conference, 430
- Paass G., 1997 Real World Computing Symposium, 118
- Sutton C., Proceedings of the 23rd Symposium on the Interface
- Tibshirani R., 1995, “Model Search and Inference by Bootstrap ‘Bumping’,”
Cited by
Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas848 · Cited by 766
Metadata
- Authors
- Chipman, H. A., George, E. I., & McCulloch, R. E.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1998-09
- Volume
- 93
- Issue
- 443
- Pages
- 935-948
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2151832869
- Cited by
- 734
- References
- 18
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- closed
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