A Skew-Normal Copula-Driven GLMM
Methodology
2017-08-01 v1
Abstract
This paper presents a method for fitting a copula-driven generalized linear mixed models. For added flexibility, the skew-normal copula is adopted for fitting. The correlation matrix of the skew-normal copula is used to capture the dependence structure within units, while the fixed and random effects coefficients are estimated through the mean of the copula. For estimation, a Monte Carlo expectation-maximization algorithm is developed. Simulations are shown alongside a real data example from the Framingham Heart Study.
Cite
@article{arxiv.1707.09565,
title = {A Skew-Normal Copula-Driven GLMM},
author = {Kalyan Das and Mohamad Elmasri and Arusharka Sen},
journal= {arXiv preprint arXiv:1707.09565},
year = {2017}
}
Comments
20 pages, 5 figures, 4 tables