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| Main Authors: | , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.00245 |
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| _version_ | 1866915216989618176 |
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| author | AghahosseinaliShirazi, Zahra Rangel, Pedro A. de Souza, Camila P. E. |
| author_facet | AghahosseinaliShirazi, Zahra Rangel, Pedro A. de Souza, Camila P. E. |
| contents | Zero-inflated count data arise in various fields, including health, biology, economics, and the social sciences. These data are often modelled using probabilistic distributions such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), or zero-inflated binomial (ZIB). To account for heterogeneity in the data, it is often useful to cluster observations into groups that may explain underlying differences in the data-generating process. This paper focuses on model-based clustering for zero-inflated counts when observations are structured in a matrix form rather than a vector. We propose a clustering framework based on mixtures of ZIP or ZINB distributions, with both the count and zero components depending on cluster assignments. Our approach incorporates covariates through a log-linear structure for the mean parameter and includes a size factor to adjust for differences in total sampling or exposure. Model parameters and cluster assignments are estimated via the Expectation-Maximization (EM) algorithm. We assess the performance of our proposed methodology through simulation studies evaluating clustering accuracy and estimator properties, followed by applications to publicly available datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model-based Clustering of Multi-Dimensional Zero-Inflated Counts via the EM Algorithm AghahosseinaliShirazi, Zahra Rangel, Pedro A. de Souza, Camila P. E. Methodology Applications Zero-inflated count data arise in various fields, including health, biology, economics, and the social sciences. These data are often modelled using probabilistic distributions such as zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), or zero-inflated binomial (ZIB). To account for heterogeneity in the data, it is often useful to cluster observations into groups that may explain underlying differences in the data-generating process. This paper focuses on model-based clustering for zero-inflated counts when observations are structured in a matrix form rather than a vector. We propose a clustering framework based on mixtures of ZIP or ZINB distributions, with both the count and zero components depending on cluster assignments. Our approach incorporates covariates through a log-linear structure for the mean parameter and includes a size factor to adjust for differences in total sampling or exposure. Model parameters and cluster assignments are estimated via the Expectation-Maximization (EM) algorithm. We assess the performance of our proposed methodology through simulation studies evaluating clustering accuracy and estimator properties, followed by applications to publicly available datasets. |
| title | Model-based Clustering of Multi-Dimensional Zero-Inflated Counts via the EM Algorithm |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2406.00245 |