Generalized generalized linear models: Convex estimation and online bounds

• URL: http://arxiv.org/abs/2304.13793v1
• Date: Wed, 26 Apr 2023 19:19:42 GMT
• Title: Generalized generalized linear models: Convex estimation and online bounds
• Authors: Anatoli Juditsky, Arkadi Nemirovski, Yao Xie, and Chen Xu
• Abstract summary: We introduce inequalities in a class of models (GL-based) models (GGLM) The proposed approach uses the operator-based approach to overcome nontemporal variation among models. We demonstrate the performance using numerical simulations and a real data example for incidents.
• Score: 11.295523372922533
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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