Efficient bayesian spatially varying coefficients modeling for censored data using the vecchia approximation

Spatially varying coefficients (SVC) models allow for marginal effects to be non-stationary over space and thus offer a higher degree of flexibility with respect to standard geostatistical models with external drift. At the same time, SVC models have the advantage that they are easily interpretable. They offer a flexible framework for understanding how the relationships between dependent and independent variables vary across space.
The most common methods for modeling such data are the Geographically Weighted Regression (GWR) and Bayesian Gaussian Process (Bayes-GP). The Bayesian SVC model, which assumes that the coefficients follow Gaussian processes, provides a rigorous approach to account for spatial non-stationarity. However, the computational cost of Bayes-GP models can be prohibitively high, even for moderately sized datasets, due to the repeated inversion of dense covariance matrices at each MCMC iteration and the high-dimensional parameter space introduced by coefficient-specific Gaussian processes. In this study, we propose an efficient Bayes-GP modeling framework leveraging the Vecchia approximation to reduce computational complexity while maintaining accuracy. The proposed method is validated on synthetic datasets and then applied to a challenging soil pollution data set in Toulouse, France, characterized by a high degree of censoring (two-thirds censored observations) and an irregular, spatially clustered sampling design.
Our results demonstrate the ability of the Vecchia-based Bayes-GP model to capture spatially varying effects and provide meaningful insights into spatial heterogeneity, even under the constraints of censored data.

The publication can be found here: doi.org/10.1007/s11075-026-02415-7

A public preprint is available here: https://arxiv.org/abs/2511.21553