10 citations · 27 across the 10 of their papers we have counts for
10 papers
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…
A possibility-theoretic solution to Basu's Bayesian--frequentist via media
Ryan Martin
Basu's via media is what he referred to as the middle road between the Bayesian and frequentist poles. He seemed skeptical that a suitable via media could be found, but I disagree.…
Fiducial inference viewed through a possibility-theoretic inferential model lens
Ryan Martin
Fisher's fiducial argument is widely viewed as a failed version of Neyman's theory of confidence limits. But Fisher's goal -- Bayesian-like probabilistic uncertainty quantification…
Empirical Bayes inference in sparse high-dimensional generalized linear models
Yiqi Tang, Ryan Martin
High-dimensional linear models have been widely studied, but the developments in high-dimensional generalized linear models, or GLMs, have been slower. In this paper, we propose an…
Elucidating Inferential Models with the Cauchy Distribution
Chuanhai Liu, Ryan Martin
Statistical inference as a formal scientific method to covert experience to knowledge has proven to be elusively difficult. While frequentist and Bayesian methodologies have been a…
Valid and efficient imprecise-probabilistic inference with partial priors, II. General framework
Ryan Martin
Bayesian inference requires specification of a single, precise prior distribution, whereas frequentist inference only accommodates a vacuous prior. Since virtually every real-world…