The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors
Open accessAbstract
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natural nested structure inherent to many model components, which defines the model component to be a flexible extension of a base model. Proper priors are defined to penalise the complexity induced by deviating from the simpler base model and are formulated after the input of a user-defined scaling parameter for that model component, both in the univariate and the multivariate case. These priors are invariant to reparameterisations, have a natural connection to Jeffreys’ priors, are designed to support Occam’s razor and seem to have excellent robustness properties, all which are highly desirable and allow us to use this approach to define default prior distributions. Through examples and theoretical results, we demonstrate the appropriateness of this approach and how it can be applied in various situations.
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Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
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
Self-citationBayesian calibration of computer models
Kennedy, M. C., & O'Hagan, A. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00294 · Cited by 4,222
Bayesian image restoration, with two applications in spatial statistics
Besag, J., York, J., & Molli�, A. (1991) · Annals of the Institute of Statistical Mathematics · doi:10.1007/bf00116466 · Cited by 4,205
Gelman, A. (2006) · Bayesian Analysis · doi:10.1214/06-ba117a · Cited by 4,050 · cited 11×
“Contrary to this, Gelman (2006), Gelman et al. (2008) argue that, when setting priors on the standard deviation of a normal distribution, heavy tails are necessary for robustness (O’Hagan and Pericchi, 2012). Unfortunately, this theory is only developed for inferring location-scale families or natural parameters in ex…”
BackgroundInfluentialThe Statistical Analysis of Compositional Data
Aitchison, J. (1982) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1982.tb01195.x · Cited by 3,405
Hastie, T., & Tibshirani, R. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat03141 · Cited by 3,156
Park, 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
Cited by
Visualization in Bayesian Workflow
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., & Gelman, A. (2019) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12378 · Cited by 1,122
Self-citationBayesian Computing with INLA: A Review
Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., & Lindgren, F. K. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054045 · Cited by 737
Self-citationA framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
Debray, T. P., Damen, J. A., Riley, R. D., Snell, K., Reitsma, J. B., Hooft, L., Collins, G. S., & Moons, K. G. (2019) · Statistical Methods in Medical Research · doi:10.1177/0962280218785504 · Cited by 278
“In line with previous recommendations,(51,55) we consider empirically-based priors for discr, cal:OE and cal:slope to rule out unreasonable values for these parameters and to allow for regularization when few studies are available for meta-analysis.(54) Therefore, we retrieved heterogeneity estimates from more than 20…”
MethodBayesian hierarchical spatial models: Implementing the Besag York Mollié model in stan
Morris, M., Wheeler-Martin, K., Simpson, D., Mooney, S. J., Gelman, A., & DiMaggio, C. (2019) · Spatial and Spatio-temporal Epidemiology · doi:10.1016/j.sste.2019.100301 · Cited by 192
Self-citationA new avenue for Bayesian inference with INLA
Van Niekerk, J., Krainski, E., Rustand, D., & Rue, H. (2023) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2023.107692 · Cited by 75
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Metadata
- Authors
- Simpson, D., Rue, H., Riebler, A., Martins, T. G., & Sørbye, S. H.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 2017-02-01
- Volume
- 32
- Issue
- 1
- Pages
- 1-28
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W1487139063
- Cited by
- 1,085
- References
- 83
- FWCI
- 56.16
- Type
- journal article
- Language
- en
- Open access
- bronze
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