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Model Determination using Predictive Distributions with Implementation via Sampling-Based Methods
Abstract
Model determination is divided into the issues of model adequacy and model selection. Predictive distributions are used to address both issues. This seems natural since, typically, prediction is a primary purpose for the chosen model. A cross-validation viewpoint is argued for. In particular, for a given model, it is proposed to validate conditional predictive distributions arising from single point deletion against observed responses. Sampling based methods are used to carry out required calculations. An example investigates the adequacy of and rather subtle choice between two sigmoidal growth models of the same dimension.
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Cites
A Common Model Selection Criterion
Draper, N. R., & Guttman, I. (1987) · Springer eBooks · doi:10.1007/978-1-4613-1885-9_14 · Cited by 8
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Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
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Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Metadata
- Authors
- Gelfand, A. E., Dey, D. K., & Chang, H.
- Venue
- Bayesian Statistics 4
- Publisher
- Oxford University PressOxford
- Published
- 1992-08-13
- Pages
- 147-167
- OpenAlex
- W2160624840
- Cited by
- 656
- References
- 1
- FWCI
- 3.93
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Gelfand, A. E., Dey, D. K., & Chang, H. (1992). Model Determination using Predictive Distributions with Implementation via Sampling-Based Methods. Bayesian Statistics 4. https://doi.org/10.1093/oso/9780198522669.003.0009
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