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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2021
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| Accès en ligne: | https://arxiv.org/abs/2107.00470 |
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| _version_ | 1866909503132270592 |
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| author | Corsini, Noemi Viroli, Cinzia |
| author_facet | Corsini, Noemi Viroli, Cinzia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_00470 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Dealing with overdispersion in multivariate count data Corsini, Noemi Viroli, Cinzia Methodology Computation 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. |
| title | Dealing with overdispersion in multivariate count data |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2107.00470 |