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Modeling Nested Copulas with GLMM Marginals for Longitudinal Data

Title:

Modeling Nested Copulas with GLMM Marginals for Longitudinal Data

Bairakdar, Roba (2017) Modeling Nested Copulas with GLMM Marginals for Longitudinal Data. Masters thesis, Concordia University.

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Abstract

A flexible approach for modeling longitudinal data is proposed. The model consists of nested bivariate copulas with Generalized Linear Mixed Models (GLMM) marginals, which are tested and validated by means of likelihood ratio tests and compared via their AICc and BIC values.
The copulas are joined together through a vine structure. Rank-based methods are used for the estimation of the copula parameters, and appropriate model validation methods are used such as the Cram�er Von Mises goodness-of-�fit test. This model allows flexibility in the choice of the marginal distributions, provided by the family of the GLMM. Additionally, a wide variety of copula families can be fi�tted to the tree structure, allowing different nested dependence structures. This methodology is tested by an application on real data in a biostatistics study.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (Masters)
Authors:Bairakdar, Roba
Institution:Concordia University
Degree Name:M. Sc.
Program:Mathematics
Date:19 December 2017
Thesis Supervisor(s):Mailhot, Melina and Ducharme, Francine
ID Code:983332
Deposited By: Roba Bairakdar
Deposited On:11 Jun 2018 03:27
Last Modified:11 Jun 2018 03:27
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