Modeling group heterogeneity in spatio-temporal data via physics-informed semiparametric regression

TL;DR

This paper introduces a physics-informed semiparametric regression model for spatio-temporal data with group structures, combining physical dynamics with latent variability, and demonstrates its effectiveness through simulations and real-world nitrogen dioxide data analysis.

Contribution

It extends mixed effect models by integrating a PDE-regularized nonparametric component to capture physical dynamics and heterogeneity in group-structured spatio-temporal data.

Findings

The model accurately captures physical and group effects in simulated data.

It outperforms existing methods in estimating nitrogen dioxide concentrations.

The approach effectively accounts for measurement heterogeneity across sensors.

Abstract

In this work we propose a novel approach for modeling spatio-temporal data characterized by group structures. In particular, we extend classical mixed effect regression models by introducing a space-time nonparametric component, regularized through a partial differential equation, to embed the physical dynamics of the underlying process, while random effects capture latent variability associated with the group structure present in the data. We propose a two-step procedure to estimate the fixed and random components of the model, relying on a functional version of the Iterative Reweighted Least Squares algorithm. We investigate the asymptotic properties of both fixed and random components, and we assess the performance of the proposed model through a simulation study, comparing it with state-of-the-art alternatives from the literature. The proposed methodology is finally applied to the study of hourly nitrogen dioxide concentration data in Lombardy (Italy), using random effects to account for measurement heterogeneity across monitoring stations equipped with different sensor technologies.