Generalized linear mixed models for strawberry inflorescence data

Cole, Diana J. and Morgan, Byron J. T. and Ridout, Martin S. (2003) Generalized linear mixed models for strawberry inflorescence data. Statistical Modelling, 3 (4). pp. 273-290. ISSN 1471-082X. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Strawberry inflorescences have a variable branching structure. This paper demonstrates how the inflorescence structure can be modelled concisely using binomial logistic generalized linear mixed models. Many different procedures exist for estimating the parameters of generalized linear mixed models, including penalized likelihood, EM, Bayesian techniques, and simulated maximum likelihood. The main methods are reviewed and compared for fitting binomial logistic generalized linear mixed models to strawberry inflorescence data. Simulations matched to the original data are used to show that a modified EM method due to Steele (1996) is clearly the best, in terms of speed and mean-squared-error performance, for data of this kind.

Item Type: Article
Uncontrolled keywords: correlated binomial; Gauss-Hermite quadrature; GLMMs; Laplace importance sampling; modified EM; penalized likelihood; random effects; simulated maximum likelihood; variance components BIAS CORRECTION; EM ALGORITHM; COMPONENT; APPROXIMATION; VARIABILITY; DISPERSION
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Judith Broom
Date Deposited: 09 Sep 2008 09:54
Last Modified: 06 May 2014 14:25
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