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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2024-02-01
|
| Series: | Journal of Statistical Software |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/4746 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850058966699081728 |
|---|---|
| author | Benjamin Schwendinger Florian Schwendinger Laura Vana |
| author_facet | Benjamin Schwendinger Florian Schwendinger Laura Vana |
| author_sort | Benjamin Schwendinger |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-3e829c7abc3c440abfae757a4a2db51f |
| institution | DOAJ |
| issn | 1548-7660 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj-art-3e829c7abc3c440abfae757a4a2db51f2025-08-20T02:51:00ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602024-02-01108110.18637/jss.v108.i07Holistic Generalized Linear ModelsBenjamin Schwendinger0Florian Schwendinger1Laura Vana2Technische Universität WienUniversity of KlagenfurtTechnische Universität Wien 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. https://www.jstatsoft.org/index.php/jss/article/view/4746 |
| spellingShingle | Benjamin Schwendinger Florian Schwendinger Laura Vana Holistic Generalized Linear Models Journal of Statistical Software |
| title | Holistic Generalized Linear Models |
| title_full | Holistic Generalized Linear Models |
| title_fullStr | Holistic Generalized Linear Models |
| title_full_unstemmed | Holistic Generalized Linear Models |
| title_short | Holistic Generalized Linear Models |
| title_sort | holistic generalized linear models |
| url | https://www.jstatsoft.org/index.php/jss/article/view/4746 |
| work_keys_str_mv | AT benjaminschwendinger holisticgeneralizedlinearmodels AT florianschwendinger holisticgeneralizedlinearmodels AT lauravana holisticgeneralizedlinearmodels |