Holistic Generalized Linear Models
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915676513370112 |
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| author | Schwendinger, Benjamin Schwendinger, Florian Vana, Laura |
| author_facet | Schwendinger, Benjamin Schwendinger, Florian Vana, Laura |
| contents | Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\textsf{R}$ package $\texttt{holiglm}$ provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art conic mixed-integer solvers, the package can reliably solve GLMs for Gaussian, binomial and Poisson responses with a multitude of holistic constraints. The high-level interface simplifies the constraint specification and can be used as a drop-in replacement for the $\texttt{stats::glm()}$ function. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_15447 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Holistic Generalized Linear Models Schwendinger, Benjamin Schwendinger, Florian Vana, Laura Machine Learning Mathematical Software Optimization and Control Holistic linear regression extends the classical best subset selection problem by adding additional constraints designed to improve the model quality. These constraints include sparsity-inducing constraints, sign-coherence constraints and linear constraints. The $\textsf{R}$ package $\texttt{holiglm}$ provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art conic mixed-integer solvers, the package can reliably solve GLMs for Gaussian, binomial and Poisson responses with a multitude of holistic constraints. The high-level interface simplifies the constraint specification and can be used as a drop-in replacement for the $\texttt{stats::glm()}$ function. |
| title | Holistic Generalized Linear Models |
| topic | Machine Learning Mathematical Software Optimization and Control |
| url | https://arxiv.org/abs/2205.15447 |