{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T13:58:14Z","timestamp":1770991094639,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T00:00:00Z","timestamp":1519257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Major Program of Hubei Provincial Department of Education for Philosophy and Social Science Research","award":["Grant No. 17ZD018"],"award-info":[{"award-number":["Grant No. 17ZD018"]}]},{"name":"The UK National Institute for Health Research Method Grant","award":["NIHR-RMOFS-2013-03-09"],"award-info":[{"award-number":["NIHR-RMOFS-2013-03-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discrete Weibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdispersion, underdispersion and covariate-specific dispersion. Maximum likelihood can be used for efficient parameter estimation. The description of the model, parameter inference and model diagnostics is accompanied by simulated and real data analyses.<\/jats:p>","DOI":"10.3390\/e20020142","type":"journal-article","created":{"date-parts":[[2018,2,22]],"date-time":"2018-02-22T12:57:16Z","timestamp":1519304236000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Simple and Adaptive Dispersion Regression Model for Count Data"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6617-2081","authenticated-orcid":false,"given":"Hadeel","family":"Klakattawi","sequence":"first","affiliation":[{"name":"Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2625-7977","authenticated-orcid":false,"given":"Veronica","family":"Vinciotti","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keming","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cameron, A.C., and Trivedi, P.K. 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