# Generalized generalized linear models: Convex estimation and online bounds

arXiv: https://arxiv.org/abs/2304.13793 · HTML: https://mlsanity.com/paper/2304.13793/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 5 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [Transfer Learning for Causal Effect Estimation](https://mlsanity.com/paper/2305.09126/) (2023)
- [Nonlinear time-series embedding by monotone variational inequality](https://mlsanity.com/paper/2406.06894/) (2024)
- [Beyond Maximum Likelihood: Variational Inequality Estimation for Generalized Linear Models](https://mlsanity.com/paper/2511.03087/) (2025)
- [Sequential statistical inference for Large Language Models: Representation, validity, and monitoring](https://mlsanity.com/paper/2606.07624/) (2026)
- [Deep spatio-temporal point processes: Advances and new directions](https://mlsanity.com/paper/2504.06364/) (2025)
- [Quickest Change-point Detection in Generalized Linear Models with Variational Estimation](https://mlsanity.com/paper/2610.16616/) (2026)

## Abstract (arXiv)

We introduce a new computational framework for estimating parameters in generalized generalized linear models (GGLM), a class of models that extends the popular generalized linear models (GLM) to account for dependencies among observations in spatio-temporal data. The proposed approach uses a monotone operator-based variational inequality method to overcome non-convexity in parameter estimation and provide guarantees for parameter recovery. The results can be applied to GLM and GGLM, focusing on spatio-temporal models. We also present online instance-based bounds using martingale concentrations inequalities. Finally, we demonstrate the performance of the algorithm using numerical simulations and a real data example for wildfire incidents.