Holistic Generalized Linear Models

Abstract

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 $\text{R}$ package $\text{holiglm}$ 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 $\text{stats::glm()}$ function.

34 pages, 2 figures, 4 tables

References in corpus (2)

• abess: A Fast Best Subset Selection Library in Python and R
• Holistic Generalized Linear Models

Cited by in corpus (2)

• Holistic Generalized Linear Models
• Automated Model Selection for Generalized Linear Models