A Flexible Quasi-Copula Distribution for Statistical Modeling
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Copulas, generalized estimating equations, and generalized linear mixed models promote the analysis of grouped data where non-normal responses are correlated. Unfortunately, parameter estimation remains challenging in these three frameworks. Based on prior work of Tonda, we derive a new class of probability density functions that allow explicit calculation of moments, marginal and conditional distributions, and the score and observed information needed in maximum likelihood estimation. Unlike true copulas, our quasi-copula model only approximately preserves marginal distributions. Simulation studies with Poisson, negative binomial, Bernoulli, and Gaussian bases demonstrate the computational and statistical virtues of the quasi-copula model and its limitations.
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