The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Markov Chain Monte Carlo Analysis of Correlated Count Data
Open accessAbstract
This article is concerned with the analysis of correlated count data. A class of models is proposed in which the correlation among the counts is represented by correlated latent effects. Special cases of the model are discussed and a tuned and efficient Markov chain Monte Carlo algorithm is developed to estimate the model under both multivariate normal and multivariate-t assumptions on the latent effects. The methods are illustrated with two real data examples of six and sixteen variate correlated counts.
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
Regression Analysis of Count Data
Cameron, A. C., & Trivedi, P. K. (2013) · doi:10.1017/cbo9781139013567 · Cited by 6,640
Econometric Analysis of Count Data
Winkelmann, R. (2003) · doi:10.1007/978-3-540-24728-9 · Cited by 945
Self-citation
Cited by
Review of statistical methods for analysing healthcare resources and costs
Mihaylova, B., Briggs, A., O'Hagan, A., & Thompson, S. G. (2011) · Health Economics · doi:10.1002/hec.1653 · Cited by 767
Karlis, D., & Xekalaki, E. (2007) · International Statistical Review · doi:10.1111/j.1751-5823.2005.tb00250.x · Cited by 324
Extending the rank likelihood for semiparametric copula estimation
D. Hoff, P. (2007) · The Annals of Applied Statistics · doi:10.1214/07-aoas107 · Cited by 297
Anwar, M. Y., Warren, J. L., & Pitzer, V. E. (2019) · The American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.18-0735 · Cited by 40
Metadata
- Authors
- Chib, S., & Winkelmann, R.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 2001-10
- Volume
- 19
- Issue
- 4
- Pages
- 428-435
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2056367065
- Cited by
- 240
- References
- 2
- FWCI
- 6.96
- Type
- journal article
- Language
- en
- Open access
- green
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: Markov Chain Monte Carlo Analysis of Correlated Count Data. https://app.cochat.ai/papers/10.1198/07350010152596673
- The paper
- Chib, S., & Winkelmann, R. (2001). Markov Chain Monte Carlo Analysis of Correlated Count Data. Journal of Business & Economic Statistics. https://doi.org/10.1198/07350010152596673
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.
