An Automated MCEM Algorithm for Hierarchical Models with Multivariate and Multitype Response Variables

Vera Georgescu, Nicolas Desassis, Samuel Soubeyrand, André Kretzschmar and Rachid Senoussi

Communications in Statistics - Theory and Methods, 2014, vol. 43, issue 17, 3698-3719

Abstract: 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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:43:y:2014:i:17:p:3698-3719

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DOI: 10.1080/03610926.2012.700372

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