Exchangeable copulas are used to model an extra-binomial variation in Bernoulli experiments with
a variable number of trials. Maximum likelihood inference procedures for the intra-cluster correlation are
constructed for several copula families. The selection of a particular model is carried out using the Akaike
information criterion (AIC). Profile likelihood confidence intervals for the intra-cluster correlation are constructed
and their performance are assessed in a simulation experiment. The sensitivity of the inference to
the specification of the copula family is also investigated through simulations. Numerical examples are presented.
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Dependence Modeling aims to provide a medium for exchanging results and ideas in the area of multivariate dependence modeling. Topics include Copula methods, environmental sciences, estimation and goodness-of-fit tests, extreme-value theory, limit laws, mass transportations, measures of association, multivariate distributions and tests, quantitative risk management, risk assessment, risk models, risk measures and stochastic orders and time series.