Gaussian random fields

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

1 min read ·
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

excursions: A Framework for Probabilistic Excursion Sets and Contour Inference

rSPDE

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

Gaussian Random Fields on Metric Graphs

-

B9 B2 L2325

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.