Dealing with overdispersion in multivariate count data

Noemi Corsini and Cinzia Viroli

Computational Statistics & Data Analysis, 2022, vol. 170, issue C

Abstract: The problem of overdispersion in multivariate count data is a challenging issue. It covers a central role mainly due to the relevance of modern technology-based data, such as Next Generation Sequencing and textual data from the web or digital collections. A comprehensive analysis of the likelihood-based models for extra-variation data is presented. Particular attention is paid to the models feasible for high-dimensional data. A new approach together with its parametric-estimation procedure is proposed. It can be viewed as 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 the proposed model and existing strategies is made through two different simulation studies and an empirical data set, that confirm a better capability to describe overdispersion.

Keywords: Extra-variation; Mixture models; Deep learning; Maximum likelihood (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000275

DOI: 10.1016/j.csda.2022.107447

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