A graphical framework for interpretable correlation matrix models
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908292183228416 |
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| author | Sterrantino, Anna Freni Rustand, Denis van Niekerk, Janet Krainski, Elias Teixeira Rue, Håvard |
| author_facet | Sterrantino, Anna Freni Rustand, Denis van Niekerk, Janet Krainski, Elias Teixeira Rue, Håvard |
| contents | In this work, we present a new approach for constructing models for correlation matrices with a user-defined graphical structure. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. We suggest an automatic approach to define a prior using a natural sequence of simpler models within the Penalized Complexity framework for the unknown parameters in these models. We illustrate this approach with three applications: a multivariate linear regression of four biomarkers, a multivariate disease mapping, and a multivariate longitudinal joint modelling. Each application underscores our method's intuitive appeal, signifying a substantial advancement toward a more cohesive and enlightening model that facilitates a meaningful interpretation of correlation matrices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_06289 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A graphical framework for interpretable correlation matrix models Sterrantino, Anna Freni Rustand, Denis van Niekerk, Janet Krainski, Elias Teixeira Rue, Håvard Methodology In this work, we present a new approach for constructing models for correlation matrices with a user-defined graphical structure. The graphical structure makes correlation matrices interpretable and avoids the quadratic increase of parameters as a function of the dimension. We suggest an automatic approach to define a prior using a natural sequence of simpler models within the Penalized Complexity framework for the unknown parameters in these models. We illustrate this approach with three applications: a multivariate linear regression of four biomarkers, a multivariate disease mapping, and a multivariate longitudinal joint modelling. Each application underscores our method's intuitive appeal, signifying a substantial advancement toward a more cohesive and enlightening model that facilitates a meaningful interpretation of correlation matrices. |
| title | A graphical framework for interpretable correlation matrix models |
| topic | Methodology |
| url | https://arxiv.org/abs/2312.06289 |