Year: 2023
Type: report
Abstract: 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.
Authors: Juditsky, Anatoli, Nemirovski, Arkadi, Xie, Yao, Xu, Chen
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: ATD: Scanning Dynamic Spatial-Temporal Discrete Events for Threat Detection, Bridging Statistical Hypothesis Tests and Deep Learning for Reliability and Computational Efficiency, CAREER: Quick Detection for Streaming Data Over Dynamic Networks, G3387330441, G4324459455, Data-Driven Optimal Police Patrol Zone Districting and Staffing, Sequential Detection and Prediction for Solar Situation Awareness in Power Networks, AI Institute for Advances in Optimization, G6127310570, G6671297155, G7238632903, G7911581280, G975068223
OpenAlex ID: W4367365500