The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Testing for Overdispersion in Poisson and Binomial Regression Models
Abstract
In this article a method for obtaining tests for overdispersion with respect to a natural exponential family is derived. The tests are designed to be powerful against arbitrary alternative mixture models where only the first two moments of the mixed distribution are specified. Various tests for extra-Poisson and extra-binomial variation are obtained as special cases; the use of a particular test may be motivated by a consideration of the mechanism through which the overdispersion may arise. The common occurrence of extra-Poisson and extra-binomial variation has been noted by several authors. However, the Poisson and binomial models remain valid in many instances and, because of their simplicity and appeal, it is of real interest to ascertain when they apply. This paper develops a unifying theory for testing for overdispersion and generalizes tests previously derived, including those by Fisher (1950), Collings and Margolin (1985), and Prentice (1986). It also shows the Pearson statistic to be a score test for overdispersion in a certain situation.
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
(2005) · Statistics for Biology and Health · doi:10.1007/0-387-27255-0_9 · Cited by 20,133
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Generalized Linear Models (2nd ed.).
Therneau, T. M., McCullagh, P., & Nelder, J. A. (1993) · Journal of the American Statistical Association · doi:10.2307/2290358 · Cited by 4,907
An Introduction to Probability Theory.
Fristedt, B., & Moran, P. A. P. (1986) · Journal of the American Statistical Association · doi:10.2307/2288022 · Cited by 3,300
Cox, D. R., & Hinkley, D. V. (1974) · doi:10.1007/978-1-4899-2887-0 · Cited by 3,089
Econometric models based on count data. Comparisons and applications of some estimators and tests
Cameron, A. C., & Trivedi, P. K. (1986) · Journal of Applied Econometrics · doi:10.1002/jae.3950010104 · Cited by 1,771
Transformation and Weighting in Regression
Carroll, R. J., & Ruppert, D. (1988) · Springer eBooks · doi:10.1007/978-1-4899-2873-3 · Cited by 1,299
Transformation and Weighting in Regression.
Lachenbruch, P. A., Carroll, R. J., & Ruppert, D. (1990) · Journal of the American Statistical Association · doi:10.2307/2289809 · Cited by 1,132
Negative binomial and mixed Poisson regression
Lawless, J. F. (1987) · Canadian Journal of Statistics · doi:10.2307/3314912 · Cited by 1,099
Extra-Binomial Variation in Logistic Linear Models
Williams, D. A. (1982) · Applied Statistics · doi:10.2307/2347977 · Cited by 798
Cited by
Linear and generalized linear mixed models
Bolker, B. M. (2015) · Ecological Statistics · doi:10.1093/acprof:oso/9780199672547.003.0014 · Cited by 339
Enscore, R. E., Biggerstaff, B. J., Brown, T. L., Fulgham, R. E., Reynolds, P. J., Engelthaler, D. M., Levy, C. E., Parmenter, R. R., Montenieri, J. A., Cheek, J. E., et al. (2002) · The American journal of tropical medicine and hygiene · doi:10.4269/ajtmh.2002.66.186 · Cited by 166
Modeling underdispersed count data with generalized Poisson regression.
Harris, T., Yang, Z., & Hardin, J. W. (2012) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x1201200412 · Cited by 150
T1DGC, O. B. O. T., Wiebe, J. C., Santana, A., Medina-Rodríguez, N., Hernández, M., Nóvoa, J., Mauricio, D., & Wägner, A. M. (2014) · Diabetologia · doi:10.1007/s00125-014-3376-8 · Cited by 51
Metadata
- Authors
- Dean, C. B.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1992-06
- Volume
- 87
- Issue
- 418
- Pages
- 451-457
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2075379868
- Cited by
- 386
- References
- 35
- FWCI
- 7.35
- Type
- journal article
- Language
- en
- Open access
- closed
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: Testing for Overdispersion in Poisson and Binomial Regression Models. https://app.cochat.ai/papers/10.1080/01621459.1992.10475225
- The paper
- Dean, C. B. (1992). Testing for Overdispersion in Poisson and Binomial Regression Models. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1992.10475225
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.
