17 citations · 79 across the 16 of their papers we have counts for
5 papers · 1 filter
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…
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…
Valid inferential models for prediction in supervised learning problems
Leonardo Cella, Ryan Martin
Prediction, where observed data is used to quantify uncertainty about a future observation, is a fundamental problem in statistics. Prediction sets with coverage probability guaran…
Direct and approximately valid probabilistic inference on a class of statistical functionals
Leonardo Cella, Ryan Martin
Existing frameworks for probabilistic inference assume the quantity of interest is the parameter of a posited statistical model. In machine learning applications, however, often th…
Validity, consonant plausibility measures, and conformal prediction
Leonardo Cella, Ryan Martin
Prediction of future observations is an important and challenging problem. The two mainstream approaches for quantifying prediction uncertainty use prediction regions and predictiv…