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dc.contributor.authorLuyts, Martial
dc.contributor.authorMolenberghs, Geert
dc.contributor.authorVerbeke, Geert
dc.contributor.authorMatthijs, Koen
dc.contributor.authorRibeiro Jr, Eduardo E.
dc.contributor.authorDemétrio, Clarice G. B.
dc.contributor.authorHinde, John
dc.identifier.citationLuyts, Martial, Molenberghs, Geert, Verbeke, Geert, Matthijs, Koen, Ribeiro Jr, Eduardo E., Demétrio, Clarice G. B., & Hinde, John. (2018). A Weibull-count approach for handling under- and overdispersed longitudinal/clustered data structures. Statistical Modelling, 19(5), 569-589. doi: 10.1177/1471082X18789992en_IE
dc.description.abstractA Weibull-model-based approach is examined to handle under- and overdispersed discrete data in a hierarchical framework. This methodology was first introduced by Nakagawa and Osaki (1975, IEEE Transactions on Reliability, 24, 300–301), and later examined for under- and overdispersion by Klakattawi et al. (2018, Entropy, 20, 142) in the univariate case. Extensions to hierarchical approaches with under- and overdispersion were left unnoted, even though they can be obtained in a simple manner. This is of particular interest when analysing clustered/longitudinal data structures, where the underlying correlation structure is often more complex compared to cross-sectional studies. In this article, a random-effects extension of the Weibull-count model is proposed and applied to two motivating case studies, originating from the clinical and sociological research fields. A goodness-of-fit evaluation of the model is provided through a comparison of some well-known count models, that is, the negative binomial, Conway–Maxwell–Poisson and double Poisson models. Empirical results show that the proposed extension flexibly fits the data, more specifically, for heavy-tailed, zero-inflated, overdispersed and correlated count data. Discrete left-skewed time-to-event data structures are also flexibly modelled using the approach, with the ability to derive direct interpretations on the median scale, provided the complementary log–log link is used. Finally, a large simulated set of data is created to examine other characteristics such as computational ease and orthogonality properties of the model, with the conclusion that the approach behaves best for highly overdispersed cases.en_IE
dc.description.sponsorshipThe authors disclosed receipt of the following financial support for the research, authorship and/or publication of this article: Financial support from the IAP research network #P7/06 of the Belgian Government (Belgian Science Policy) is gratefully acknowledged. This work was partially supported by CNPq, a Brazilian science funding agency. The research leading to these results has also received funding from KU Leuven GOA project: New approaches to the social dynamics of long term fertility change.en_IE
dc.publisherSAGE Publicationsen_IE
dc.relation.ispartofStatistical Modellingen
dc.subjectlongitudinal profilesen_IE
dc.subjectclustering, dispersionen_IE
dc.subjectrandom effectsen_IE
dc.subjectweibull-count approachen_IE
dc.titleA Weibull-count approach for handling under- and overdispersed longitudinal/clustered data structuresen_IE
dc.local.contactJohn Philip Hinde, Mathematics, Áras De Brún, Nui Galway. 2043 Email:

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