3 papers
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.AP2026
Accounting for variable detection functions in temporal abundance modeling via transfer learning
Kevin M. Collins, Erin M. Schliep, Tyler Wagner +1
Relative abundance, measured as the number of animals caught per unit of sampling effort (CPUE), is commonly used to monitor fish and wildlife populations, largely because sampling…
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…