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stat.ME2026
Valid and efficient possibilistic fusion
Leonardo Cella
Besides the classical motivation of fusing evidence from multiple sources, modern inferential procedures based on randomization, resampling, and data splitting often introduce anal…
stat.ME2026
Prior- and likelihood-free probabilistic inference with finite-sample calibration guarantees
Leonardo Cella, Emily C. Hector
Motivated by parametric models for which the likelihood is analytically unavailable, numerically unstable, or prohibitively expensive to compute or optimize, we develop a prior- an…
stat.ME2025
Divide-and-conquer with finite sample sizes: valid and efficient possibilistic inference
Emily C. Hector, Leonardo Cella, Ryan Martin
Divide-and-conquer methods use large-sample approximations to provide frequentist guarantees when each block of data is both small enough to facilitate efficient computation and la…