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Sampling and Bayes' Inference in Scientific Modelling and Robustness
Abstract
Abstract : Scientific learning is an iterative process employing Criticism and Estimation. Correspondingly the formulated model factors into two complimentary parts - a predictive part allowing model criticism, and a Bayes posterior part allowing estimation. Implications for significance tests, the theory of precise measurement, and for ridge estimates are considered. Predictive checking functions for transformation, serial correlation, bad values, and their relation with Bayesian options are considered. Robustness is seen from a Bayesian viewpoint and examples are given. For the bad value problem a comparison with M estimators is made. (Author)
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Cited by
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Rue, H., Martino, S., & Chopin, N. (2009) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2008.00700.x · Cited by 5,279
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050
“There is also room to generalize these distributions to variance matrices in multivariate hierarchical models, going beyond the commonly-used inverse-Wishart family of prior distributions (Box and Tiao, 1973), which has problems similar to the inverse-gamma for scalar variances. Noninformative or weakly informative co…”
BackgroundHoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Metadata
- Authors
- Box, G. E. P.
- Venue
- Journal of the Royal Statistical Society. Series A (General)
- Publisher
- JSTOR
- Published
- 1980
- Volume
- 143
- Issue
- 4
- Pages
- 383
- ISSN
- 0035-9238 (ISSN-L)
- OpenAlex
- W1526262927
- Cited by
- 924
- References
- 0
- FWCI
- 20.13
- Type
- journal article
- Language
- en
- Open access
- closed
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