# Dealing with overdispersion in multivariate count data

arXiv: https://arxiv.org/abs/2107.00470 · HTML: https://mlsanity.com/paper/2107.00470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Hierarchical mixtures of Unigram models for short text clustering: The role of Beta-Liouville priors](https://mlsanity.com/paper/2410.21862/) (2024)

## Abstract (arXiv)

The problem of overdispersion in multivariate count data is a challenging issue. Nowadays, it covers a central role mainly due to the relevance of modern technologies data, such as Next Generation Sequencing and textual data from the web or digital collections. This work presents a comprehensive analysis of the likelihood-based models for extra-variation data proposed in the scientific literature. Particular attention will be paid to the models feasible for high-dimensional data. A new approach together with its parametric-estimation procedure is proposed. It is a deeper version of the Dirichlet-Multinomial distribution and it leads to important results allowing to get a better approximation of the observed variability. A significative comparison of these models is made through two different simulation studies that both confirm that the new model considered in this work allows to achieve the best results.