Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/6974
Title: A Comparison of Methods for Analysing Correlated Count Data
Contributor(s): Alston, Clair (author); Murison, Robert (supervisor); Smith, David (supervisor)
Conferred Date: 1998
Copyright Date: 1997
Open Access: Yes
Handle Link: https://hdl.handle.net/1959.11/6974
Abstract: This thesis considers extensions of Generalized Linear Models (Nelder and Wedderburn, 1972) to incorporate correlated count data. Of particular interest is the Poisson random effects model which is commonly solved by approximate methods due to the complexity of calculations in maximum likelihood estimation (Diggle, Liang and Zeger, 1994, pl73-5). The methods considered fall into 4 categories; 1. quasi-likelihood techniques, (Schall, 1991), (Breslow and Clayton, 1993), 2. overdispersion models, (Van de Ven and Weber, 1995) 3. generalized estimating equations, (Liang and Zeger, 1986), and 4. Markov Chain Monte Carlo techniques, (Zeger and Karim, 1991).
Publication Type: Thesis Masters Research
Rights Statement: Copyright 1997 - Clair Alston
HERDC Category Description: T1 Thesis - Masters Degree by Research
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