Paper ID: 2205.15447

Holistic Generalized Linear Models

Benjamin Schwendinger, Florian Schwendinger, Laura Vana

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.

Submitted: May 30, 2022