Semi-Parametric Inference for Doubly Stochastic Spatial Point Processes: An Approximate Penalized Poisson Likelihood Approach
Researchers have developed a new semi-parametric inference method for doubly-stochastic spatial point processes, which are used to model event occurrences in spatial domains. This approach offers computational efficiency and avoids restrictive assumptions about the intensity function, unlike previous methods. The technique achieves consistent and asymptotically normal estimates for covariate effects, even with model misspecification, and provides a valid inference procedure. Simulations and an application to Seattle crime data indicate improved prediction accuracy over existing alternatives. AI
IMPACT Introduces a more efficient and flexible statistical method for spatial data analysis, potentially improving predictive accuracy in applications like crime mapping.