Elmasri, Mohamad (2012) A Skew-Normal Copula-Driven Generalized Linear Mixed Model for Longitudinal Data. Masters thesis, Concordia University.
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Using the advancements of Arellano-Valle et al. , which characterize the likelihood function of a linear mixed model (LMM) under a skew-normal distribution for the random effects, this thesis attempt to construct a copula-driven generalized linear mixed model (GLMM). Assuming a multivariate distribution from the exponential family for the response variable and a skew-normal copula, we drive a complete characterization of the general likelihood function. For estimation, we apply a Monte Carlo expectation maximization (MC-EM) algorithm. Some special cases are discussed, in particular, the exponential and gamma distributions. Simulations with multiple link functions are shown alongside a real data example from the Framingham Heart
|Divisions:||Concordia University > Faculty of Arts and Science > Mathematics and Statistics|
|Item Type:||Thesis (Masters)|
|Degree Name:||M. Sc.|
|Date:||15 April 2012|
|Thesis Supervisor(s):||Sen, Arusharka|
|Keywords:||Generalized Linear Mixed models, Copula, Skew-Normal distribution, Exponential family.|
|Deposited By:||MOHAMAD ELMASRI|
|Deposited On:||20 Jun 2012 11:38|
|Last Modified:||15 Nov 2012 17:01|
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