Hierarchical Bayesian Models for Multiple Count Data

Authors

  • Radu Tunaru Economics Department, London Metropolitan University

DOI:

https://doi.org/10.17713/ajs.v31i2&3.484

Abstract

The aim of this paper is to develop a model for analyzing multiple response models for count data and that may take into account complex correlation structures. The model is specified hierarchically in several layers and can be used for sparse data as it is shown in the second part of the paper. It is a discrete multivariate response approach regarding the left side of models equations. Markov Chain Monte Carlo techniques are needed for extracting inferential results. The possible correlation between different counts is more
general than the one used in repeated measurements or longitudinal studies framework.

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Published

2016-04-03

How to Cite

Tunaru, R. (2016). Hierarchical Bayesian Models for Multiple Count Data. Austrian Journal of Statistics, 31(2&3), 221–229. https://doi.org/10.17713/ajs.v31i2&3.484

Issue

Vol. 31 No. 2&3 (2002)

Section

Articles

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