# Holistic Generalized Linear Models

arXiv: https://arxiv.org/abs/2205.15447 · HTML: https://mlsanity.com/paper/2205.15447/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 30 May 2022.

## Builds on (most cited first, 6 of 6)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Exact post-selection inference, with application to the lasso](https://mlsanity.com/paper/1311.6238/) (2013)
- [Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms](https://mlsanity.com/paper/1803.01454/) (2018)
- [abess: A Fast Best Subset Selection Library in Python and R](https://mlsanity.com/paper/2110.09697/) (2021)
- [Scalable Holistic Linear Regression](https://mlsanity.com/paper/1902.03272/) (2019)
- [A Splicing Approach to Best Subset of Groups Selection](https://mlsanity.com/paper/2104.12576/) (2021)

## Built on by (most cited first, 2 of 2)

- [Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift](https://mlsanity.com/paper/2504.02694/) (2025)
- [Automated Model Selection for Generalized Linear Models](https://mlsanity.com/paper/2404.16560/) (2024)

## Abstract (arXiv)

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 textsfR package textttholiglm 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 textttstats::glm() function.