67: Distinguishing High/Low Exposure in Marked Spatial Point Processes Using a Bayesian Mixture Model

Music City Center 

Assessing the effect of environmental exposures on adverse health outcomes needs flexible statistical frameworks that capture heterogeneous exposure subpopulations. We propose a Bayesian mixture extension of the Marked Log‐Gaussian Cox Process (LGCP) to accommodate high‐ and low‐exposure groups, enabling distinct intensity and mark distributions within a unified model. This approach is compared against a standard, non‐mixture LGCP to investigate the benefits of explicitly modeling multiple exposure strata.
We conduct a comprehensive simulation study to evaluate parameter estimation and predictive performance under both models, implementing an MCMC‐based inference scheme to characterize the posterior distributions of key parameters. The proposed framework is illustrated on simulated datasets designed to emulate real‐world exposure heterogeneity. The presentation will focus on the extent to which the mixture component can reduce bias and enhance interpretability when substantial within‐population variation is present.

Bayesian Mixture Model

Marked Log‐Gaussian Cox Process

Exposure Heterogeneity

Spatial Point Process

MCMC

Simulation Study 

Section on Statistics and the Environment