5 citations · 7 across the 6 of their papers we have counts for
6 papers
Universal Inference for model selection on networks
Eric Yanchenko, Jonathan P. Williams, Ryan Martin
Model selection and hypothesis testing are important tasks on networks. A key challenge lies in the inherent dependence in network data, as well as the fact that typically only a s…
Hypothesis testing for community structure in temporal networks using e-values
Eric Yanchenko, Jonathan P. Williams, Ryan Martin
Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community st…
Asymptotic efficiency of inferential models and a possibilistic Bernstein--von Mises theorem
Ryan Martin, Jonathan P. Williams
The inferential model (IM) framework offers an alternative to the classical probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. A key d…
Multiple Testing in Generalized Universal Inference
Neil Dey, Ryan Martin, Jonathan P. Williams
Compared to p-values, e-values provably guarantee safe, valid inference. If the goal is to test multiple hypotheses simultaneously, one can construct e-values for each individual t…
Large-sample theory for inferential models: a possibilistic Bernstein--von Mises theorem
Ryan Martin, Jonathan P. Williams
The inferential model (IM) framework offers alternatives to the familiar probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. Allowing t…
Generalized Universal Inference on Risk Minimizers
Neil Dey, Ryan Martin, Jonathan P. Williams
A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditiona…