Holistic Generalized Linear Models
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 R package holiglm provides functiona...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2024-02-01
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| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4746 |
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| Summary: | 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 R package holiglm provides functionality to model and fit holistic generalized linear models. By making use of state-of-the-art mixed-integer conic solvers, the package can reliably solve generalized linear models 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 stats::glm() function.
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| ISSN: | 1548-7660 |