Operator-matched spatial regression
Abstract
A standard spatial regression model Y = X⊤β+ U, with covariates X and a Gaussian random field U with Matérn covariance, decouples covariate effects from spatial dependence. In several applications this is mechanistically implausible, as covariates likely drive the dynamics generating the residual covariance, and it is statistically problematic, since the generalised least-squares estimator of β may be biased by spatial self-confounding. We propose operator-matched spatial regression, where covariates enter both pointwise and as deterministic forcings in a stochastic partial differential equation (SPDE) that generates the random effect, partitioning the apparent effect into local and operator-mediated components. The formulation arises as the steady state of a transport--relaxation equation forced by X, giving the parameters physical interpretations. We show that self-confounding is resolved by construction and that maximum likelihood inference for the regression coefficients, implementable through standard covariance-based methods, is calibrated when the operator parameters are known and, under expanding-domain asymptotics, when they are jointly estimated. A computationally efficient finite element implementation is introduced and justified via bounds on the discretisation error. Applications to temperature--altitude regression and air-quality monitoring show that the method matches or outperforms alternatives and produces physically meaningful results.
Related
- Koopman operator learning for predictive control via Khatri-Rao kernel regression
- Machine learning and spatial regression approaches to estimating willingness to pay for ecosystem services in Jakarta and Taoyuan
- Exploiting Locally Exogeneous Variation in Spatial Data: Linear Regression with Many Small Units
- Spatial Functional k-Nearest-Neighbour Regression under Polynomial Dependence
- Testing for point-impact effects in a spatial semi-functional linear regression model
BibTeX
@misc{bolin2026operatormatched,
title = {{Operator-matched spatial regression}},
author = {David Bolin},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12094},
eprint = {2609.12094},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12094},
}
RIS
TY - UNPB
TI - Operator-matched spatial regression
AU - David Bolin
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.12094
UR - https://arxiv.org/abs/2609.12094
AB - A standard spatial regression model Y = X^⊤β+ U, with covariates X and a Gaussian random field U with Matérn covariance, decouples covariate effects from spatial dependence. In several applications this is mechanistically implausible, as covariates likely drive the dynamics generating the residual covariance, and it is statistically problematic, since the generalised least-squares estimator of β may be biased by spatial self-confounding. We propose operator-matched spatial regression, where covariates enter both pointwise and as deterministic forcings in a stochastic partial differential equation (SPDE) that generates the random effect, partitioning the apparent effect into local and operator-mediated components. The formulation arises as the steady state of a transport--relaxation equation forced by X, giving the parameters physical interpretations. We show that self-confounding is resolved by construction and that maximum likelihood inference for the regression coefficients, implementable through standard covariance-based methods, is calibrated when the operator parameters are known and, under expanding-domain asymptotics, when they are jointly estimated. A computationally efficient finite element implementation is introduced and justified via bounds on the discretisation error. Applications to temperature--altitude regression and air-quality monitoring show that the method matches or outperforms alternatives and produces physically meaningful results.
ER -