一个斜正态Copula驱动的GLMM
A Skew-Normal Copula-Driven GLMM
摘要：
提出了一种拟合Copula驱动的广义线性混合模型的方法。为了增加灵活性，采用斜态Copula进行拟合。偏态Copula的相关矩阵用于捕获单位内的依赖结构，而固定和随机效应系数通过Copula的平均值来估计。对于估计，蒙特卡罗期望最大化算法的开发。模拟显示旁边的一个真实数据的例子，从心脏研究。
英文摘要：
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.
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