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Dealing with overdispersion in multivariate count data

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  • Corsini, Noemi
  • Viroli, Cinzia

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.

Suggested Citation

  • Corsini, Noemi & Viroli, Cinzia, 2022. "Dealing with overdispersion in multivariate count data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:csdana:v:170:y:2022:i:c:s0167947322000275
    DOI: 10.1016/j.csda.2022.107447
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    References listed on IDEAS

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    7. Neerchal, Nagaraj K. & Morel, Jorge G., 2005. "An improved method for the computation of maximum likeliood estimates for multinomial overdispersion models," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 33-43, April.
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    Cited by:

    1. Motegi, Ryosuke & Seki, Yoichi, 2023. "SMLSOM: The shrinking maximum likelihood self-organizing map," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).

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