Gaussian random fields

Spatial Self-Confounding: Smoothness-related estimation bias in spatial regression models

1 min read · Thu, Oct 30 2025

Spatial regression Maximum Likelihood Estimation Gaussian random fields spatial statistics

Spatial regression models are widely used to capture the relationship between observations and covariates, employing Gaussian random fields to account for spatial variability not explained by the covariates. A new study by researchers David Bolin and Jonas Wallin addresses a critical yet often overlooked problem in these models: smoothness-related spatial self-confounding. The work examines how misspecified covariates, particularly when there are differences in smoothness between variables, can lead to severe and counter-intuitive biases in the estimation of regression parameters. These

excursions

Research Resources

latent Gaussian models spatial statistics applied statistics bayesian inference R-INLA Gaussian random fields excursion sets

excursions: A Framework for Probabilistic Excursion Sets and Contour Inference

rSPDE

Research Resources

spatial statistics applied statistics Gaussian random fields SPDEs bayesian inference geostatistics

rSPDE: A Computational Framework for Rational Approximations of Fractional Stochastic Partial Differential Equations

Gaussian Random Fields on Metric Graphs

David Bolin, Professor, Statistics

Sep 11, 12:00 - 13:00

B9 B2 L2325

Gaussian random fields Metric graphs Statistical Modeling

This talk presents a comprehensive mathematical and statistical theory, along with user-friendly software, for modeling data with Gaussian random fields on metric graphs by developing valid covariance functions based on network distance.