17 citations · 79 across the 16 of their papers we have counts for
16 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…
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…
Fusing independent inferential models in a black-box manner
Leonardo Cella
Inferential models (IMs) represent a novel possibilistic approach for achieving provably valid statistical inference. This paper introduces a general framework for fusing independe…
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…
Possibility-theoretic statistical inference offers performance and probativeness assurances
Leonardo Cella, Ryan Martin
Statisticians are largely focused on developing methods that perform well in a frequentist sense -- even the Bayesians. But the widely-publicized replication crisis suggests that t…