Holistic Generalized Linear Models
Dublin Core
Title
Holistic Generalized Linear Models
Subject
algorithmic regression, best subset selection, conic programming, holistic constraints, optimization, R.
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.
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.
Creator
Benjamin Schwendinger
Source
https://www.jstatsoft.org/article/view/v108i07
Publisher
Technische Universität
Wien
Wien
Date
February 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Benjamin Schwendinger, “Holistic Generalized Linear Models,” Repository Horizon University Indonesia, accessed April 7, 2025, https://repository.horizon.ac.id/items/show/8320.