Locally calibrated and mesh-free inference for spatial point distributions: closed-form null, contamination law, and detectability threshold

Local inference for spatial point distributions is dominated by Monte Carlo calibration. We develop an alternative based on the Tweedie--Miyasawa identities of empirical Bayes, which relate locally weighted moments of a point distribution under a Gaussian kernel to derivatives of its log-intensity i