A Skew‐normal copula‐driven GLMM. (November 2016)

• Record Type: Journal Article
• Authors: Das, Kalyan; Elmasri, Mohamad; Sen, Arusharka
• Is Part Of: Statistica Neerlandica. Volume 70:Number 4(2016:Nov.)
• Page Start: 396
• Page End: 413
• Publication Date: 2016-11
• Subjects: EM algorithm -- Gaussian copula -- generalized linear mixed models -- Monte Carlo -- skew‐normal.; Statistics -- Periodicals; 519.5; 314.92
• DOI: 10.1111/stan.12092
• Languages: English
• ISSNs: 0039-0402

Abstract

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.