4 papers
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…
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…
Computationally efficient variational-like approximations of possibilistic inferential models
Leonardo Cella, Ryan Martin
Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite the IM framework's theoretical and foundational advantages, efficien…
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…