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An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables. (English) Zbl 1302.62072

Summary: In this article, we consider a model allowing the analysis of multivariate data, which can contain data attributes of different types (e.g., continuous, discrete, binary). This model is a two-level hierarchical model which supports a wide range of correlation structures and can accommodate overdispersed data. Maximum likelihood estimation of the model parameters is achieved with an automated Monte Carlo expectation maximization algorithm. Our method is tested in a simulation study in the bivariate case and applied to a data set dealing with beehive activity.

MSC:

62F99 Parametric inference
62H12 Estimation in multivariate analysis
62L12 Sequential estimation
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