Fast Estimation of the Composite Link Model for Multidimensional Grouped Counts
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911317025095680 |
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| author | Camarda, Carlo G. Durbán, María |
| author_facet | Camarda, Carlo G. Durbán, María |
| contents | This paper presents a significant advancement in the estimation of the Composite Link Model within a penalized likelihood framework, specifically designed to address indirect observations of grouped count data. While the model is effective in these contexts, its application becomes computationally challenging in large, high-dimensional settings. To overcome this, we propose a reformulated iterative estimation procedure that leverages Generalized Linear Array Models, enabling the disaggregation and smooth estimation of latent distributions in multidimensional data. Through simulation studies and applications to high-dimensional mortality datasets, we demonstrate the model's capability to capture fine-grained patterns while comparing its computational performance to the conventional algorithm. The proposed methodology offers notable improvements in computational speed, storage efficiency, and practical applicability, making it suitable for a wide range of fields in which high-dimensional data are provided in grouped formats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04956 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Estimation of the Composite Link Model for Multidimensional Grouped Counts Camarda, Carlo G. Durbán, María Methodology Computation This paper presents a significant advancement in the estimation of the Composite Link Model within a penalized likelihood framework, specifically designed to address indirect observations of grouped count data. While the model is effective in these contexts, its application becomes computationally challenging in large, high-dimensional settings. To overcome this, we propose a reformulated iterative estimation procedure that leverages Generalized Linear Array Models, enabling the disaggregation and smooth estimation of latent distributions in multidimensional data. Through simulation studies and applications to high-dimensional mortality datasets, we demonstrate the model's capability to capture fine-grained patterns while comparing its computational performance to the conventional algorithm. The proposed methodology offers notable improvements in computational speed, storage efficiency, and practical applicability, making it suitable for a wide range of fields in which high-dimensional data are provided in grouped formats. |
| title | Fast Estimation of the Composite Link Model for Multidimensional Grouped Counts |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2412.04956 |