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A Skew-Normal Copula-Driven Generalized Linear Mixed Model for Longitudinal Data

Title:

A Skew-Normal Copula-Driven Generalized Linear Mixed Model for Longitudinal Data

Elmasri, Mohamad (2012) A Skew-Normal Copula-Driven Generalized Linear Mixed Model for Longitudinal Data. Masters thesis, Concordia University.

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Abstract

Using the advancements of Arellano-Valle et al. [2005], 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
Study.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (Masters)
Authors:Elmasri, Mohamad
Institution:Concordia University
Degree Name:M. Sc.
Program:Mathematics
Date:15 April 2012
Thesis Supervisor(s):Sen, Arusharka
Keywords:Generalized Linear Mixed models, Copula, Skew-Normal distribution, Exponential family.
ID Code:973992
Deposited By:MOHAMAD ELMASRI
Deposited On:20 Jun 2012 11:38
Last Modified:15 Nov 2012 17:01

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  • A Skew-Normal Copula-Driven Generalized Linear Mixed Model for Longitudinal Data. (deposited 20 Jun 2012 11:38) [Currently Displayed]
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