The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Tests for Detecting Overdispersion in Poisson Regression Models
Abstract
Poisson regression models are widely used in analyzing count data. This article develops tests for detecting extra-Poisson variation in such situations. The tests can be obtained as score tests against arbitrary mixed Poisson alternatives and are generalizations of tests of Fisher (1950) and Collings and Margolin (1985). Accurate approximations for computing significance levels are given, and the power of the tests against negative binomial alternatives is compared with those of the Pearson and deviance statistics. One way to test for extra-Poisson variation is to fit models that parametrically incorporate and then test for the absence of such variation within the models; for example, negative binomial models can be used in this way (Cameron and Trivedi 1986; Lawless 1987a). The tests in this article require only the Poisson model to be fitted. Two test statistics are developed that are motivated partly by a desire to have good distributional approximations for computing significance levels. Simulations suggest that one of the statistics should be satisfactory for testing extra-Poisson variation in most practical situations involving Poisson regression 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
(2005) · Statistics for Biology and Health · doi:10.1007/0-387-27255-0_9 · Cited by 20,133
Linear Statistical Inference and its Applications: Second Editon
Rao, C. R. (1973) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316436 · Cited by 10,489
Continuous Univariate Distributions.
Omori, Y., Johnson, N. L., Kotz, S., & Balakrishnan, N. (1995) · Journal of the American Statistical Association · doi:10.2307/2291547 · Cited by 9,084
Linear Statistical Inference and Its Applications.
Robertson, C. A., & Rao, C. R. (1975) · Biometrics · doi:10.2307/2529568 · Cited by 3,598
Econometric Models for Count Data with an Application to the Patents-R & D Relationship
Hausman, J., Hall, B. H., & Griliches, Z. (1984) · Econometrica · doi:10.2307/1911191 · Cited by 3,393
Cox, D. R., & Hinkley, D. V. (1974) · doi:10.1007/978-1-4899-2887-0 · Cited by 3,089
McCullagh, P., & Nelder, J. A. (1983) · Springer eBooks · doi:10.1007/978-1-4899-3244-0 · Cited by 1,980
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
Negative binomial and mixed Poisson regression
Lawless, J. F. (1987) · Canadian Journal of Statistics · doi:10.2307/3314912 · Cited by 1,099
Self-citationExtra-Poisson Variation in Log-Linear Models
Breslow, N. E. (1984) · Applied Statistics · doi:10.2307/2347661 · Cited by 746
Cited by
Regression-based tests for overdispersion in the Poisson model
Cameron, A. C., & Trivedi, P. K. (1990) · Journal of Econometrics · doi:10.1016/0304-4076(90)90014-k · Cited by 1,192
Metadata
- Authors
- Dean, C., & Lawless, J. F.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1989-06
- Volume
- 84
- Issue
- 406
- Pages
- 467-472
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2007719232
- Cited by
- 342
- References
- 27
- FWCI
- 8.73
- 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: Tests for Detecting Overdispersion in Poisson Regression Models. https://app.cochat.ai/papers/10.1080/01621459.1989.10478792
- The paper
- Dean, C., & Lawless, J. F. (1989). Tests for Detecting Overdispersion in Poisson Regression Models. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1989.10478792
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.