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Title: | Fast two-stage estimator for clustered count data with overdispersion | Authors: | Florez, Alvaro J. MOLENBERGHS, Geert VERBEKE, Geert Kenward, Michael G. Mamouris, Pavlos Vaes, Bert |
Issue Date: | 2019 | Publisher: | TAYLOR & FRANCIS LTD | Source: | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 89(14), p. 2678-2693 | Abstract: | Clustered count data are commonly analysed by the generalized linear mixed model (GLMM). Here, the correlation due to clustering and some overdispersion is captured by the inclusion of cluster-specific normally distributed random effects. Often, the model does not capture the variability completely. Therefore, the GLMM can be extended by including a set of gamma random effects. Routinely, the GLMM is fitted by maximizing the marginal likelihood. However, this process is computationally intensive. Although feasible with medium to large data, it can be too time-consuming or computationally intractable with very large data. Therefore, a fast two-stage estimator for correlated, overdispersed count data is proposed. It is rooted in the split-sample methodology. Based on a simulation study, it shows good statistical properties. Furthermore, it is computationally much faster than the full maximum likelihood estimator. The approach is illustrated using a large dataset belonging to a network of Belgian general practices. | Notes: | [Florez, Alvaro J.; Molenberghs, Geert; Verbeke, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert; Verbeke, Geert] Katholieke Univ Leuven, I BioStat, Leuven, Belgium. [Kenward, Michael G.] London Sch Hyg & Trop Med, London, England. [Mamouris, Pavlos; Vaes, Bert] Katholieke Univ Leuven, Acad Ctr Huisartsgeneeskunde, Leuven, Belgium. | Keywords: | Generalized linear mixed model; hierarchical data; negative binomial model; poisson model; random effects;Generalized linear mixed model; hierarchical data; negative binomial model; poisson model; random effects | Document URI: | http://hdl.handle.net/1942/29133 | ISSN: | 0094-9655 | e-ISSN: | 1563-5163 | DOI: | 10.1080/00949655.2019.1630411 | ISI #: | 000475658600005 | Rights: | 2019 Informa UK Limited, trading as Taylor & Francis Group | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2020 |
Appears in Collections: | Research publications |
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10.1080@00949655.2019.1630411.pdf Restricted Access | Published version | 1.7 MB | Adobe PDF | View/Open Request a copy |
Fast two-stage.pdf | Peer-reviewed author version | 241.83 kB | peer-reviewed author version | View/Open |
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