 |
 |
Article dans une revue
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.
Mots-clés :
Continuous data
Poisson-log normal distribution
Littérature citée [26 références]
https://hal-mines-paristech.archives-ouvertes.fr/hal-01115524
Contributeur : Nicolas Desassis <>
Soumis le : mercredi 27 mai 2020 - 04:45:20
Dernière modification le : jeudi 24 septembre 2020 - 16:34:18
Contributeur : Nicolas Desassis <>
Soumis le : mercredi 27 mai 2020 - 04:45:20
Dernière modification le : jeudi 24 septembre 2020 - 16:34:18
Fichier
MCEM_multivariate POSTPRINT_1....
Fichiers produits par l'(les) auteur(s)