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Journal articles
An automated MCEM algorithm for hierarchical models with multivariate and multitype response variables.
Abstract : In this paper, 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 (MCEM) algorithm. Our method is tested in a simulation study in the bivariate case and applied to a dataset dealing with beehive activity.
Cited literature [26 references]
https://hal-mines-paristech.archives-ouvertes.fr/hal-01115524
Contributor : Nicolas Desassis <>
Submitted on : Wednesday, May 27, 2020 - 4:45:20 AM
Last modification on : Tuesday, March 2, 2021 - 1:20:09 PM
Contributor : Nicolas Desassis <>
Submitted on : Wednesday, May 27, 2020 - 4:45:20 AM
Last modification on : Tuesday, March 2, 2021 - 1:20:09 PM
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