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http://hdl.handle.net/1942/42177
Title: | A combined overdispersed longitudinal model for nominal data | Authors: | Sercundes, Ricardo K. MOLENBERGHS, Geert VERBEKE, Geert Demetrio, Clarice G. B. da Silva, Sila C. Moral, Rafael A. |
Issue Date: | 2023 | Publisher: | SAGE PUBLICATIONS LTD | Source: | STATISTICAL MODELLING, | Status: | Early view | Abstract: | Longitudinal studies involving nominal outcomes are carried out in various scientific areas. These outcomes are frequently modelled using a generalized linear mixed modelling (GLMM) framework. This widely used approach allows for the modelling of the hierarchy in the data to accommodate different degrees of overdispersion. In this article, a combined model (CM) that takes into account overdispersion and clustering through two separate sets of random effects is formulated. Maximum likelihood estimation with analytic-numerical integration is used to estimate the model parameters. To examine the relative performance of the CM and the GLMM, simulation studies were carried out, exploring scenarios with different sample sizes, types of random effects, and overdispersion. Both models were applied to a real dataset obtained from an experiment in agriculture. We also provide an implementation of these models through SAS code. | Notes: | Moral, RA (corresponding author), Maynooth Univ, Dept Math & Stat, Maynooth, County Kildare, Ireland. rafael.deandrademoral@mu.ie |
Keywords: | Multinomial distribution;beta distribution;hierarchical data | Document URI: | http://hdl.handle.net/1942/42177 | ISSN: | 1471-082X | e-ISSN: | 1477-0342 | DOI: | 10.1177/1471082X231209361 | ISI #: | 001128931300001 | Rights: | 2023 The Author(s). Open access | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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A combined overdispersed longitudinal model for nominal data.pdf | Early view | 378.39 kB | Adobe PDF | View/Open |
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