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stat.ME2026
Analyzing spatial point processes degraded by displacement and imperfect detection
Kevin M. Collins, Erin M. Schliep, Alan E. Gelfand +3
Spatial point processes are a valuable tool for probabilistic modeling to explain location data. However, the data themselves are often observed imperfectly. In order to perform ac…
stat.ME2026
Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
Dongjae Son, Brian J. Reich, Erin M. Schliep +2
Marine Protected Areas (MPAs) have been established globally to conserve marine resources. Given their maintenance costs and impact on commercial fishing, it is critical to evaluat…
stat.ME2025
Efficient Bayesian Inference for Spatial Point Patterns Using the Palm Likelihood
Kevin M. Collins, Erin M. Schliep
Bayesian inference for spatial point patterns is often hindered computationally by intractable likelihoods. In the frequentist literature, estimating equations utilizing pseudolike…