The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Deviance information criteria for missing data models
Open accessAbstract
The deviance information criterion (DIC) introduced by Spiegelhalter et al.(2002) for model assessment and model comparison is directly inspired by linear and generalised linear models, but it is open to different possible variations in the setting of missing data models, depending in particular on whether or not the missing variables are treated as parameters. In this paper, we reassess the criterion for such models and compare different DIC constructions, testing the behaviour of these various extensions in the cases of mixtures of distributions and random effect models.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cited by
Understanding predictive information criteria for Bayesian models
Gelman, A., Hwang, J., & Vehtari, A. (2014) · Statistics and Computing · doi:10.1007/s11222-013-9416-2 · Cited by 2,104
“Various difficulties have been noted with DIC (see Celeux et al., 2006, Plummer, 2008, and much of the discussion of Spiegelhalter et al., 2002) but there has been no consensus on an alternative.”
BackgroundEcological models and data in R
Bolker, B. M. (2008) · doi:10.2307/j.ctvcm4g37 · Cited by 1,297
Dynamic Structural Equation Models
Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2017.1406803 · Cited by 985
Zero tolerance ecology: improving ecological inference by modelling the source of zero observations
Martin, T. G., Wintle, B. A., Rhodes, J. R., Kuhnert, P. M., Field, S. A., Low‐Choy, S. J., Tyre, A. J., & Possingham, H. P. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00826.x · Cited by 935
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
Metadata
- Authors
- Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M.
- Venue
- Bayesian Analysis
- Publisher
- Institute of Mathematical Statistics
- Published
- 2006-12-01
- Volume
- 1
- Issue
- 4
- Pages
- 651-673
- ISSN
- 1931-6690 (ISSN-L)
- 1936-0975
- OpenAlex
- W2015558203
- Cited by
- 896
- References
- 1
- FWCI
- 28.98
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Deviance information criteria for missing data models. https://app.cochat.ai/papers/10.1214/06-ba122
- The paper
- Celeux, G., Forbes, F., Robert, C. P., & Titterington, D. M. (2006). Deviance information criteria for missing data models. Bayesian Analysis. https://doi.org/10.1214/06-ba122
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
