6 papers
Nonisomorphic Graphs Can Share an Arbitrarily Large Fraction of Their Vertex-Deleted Cards
Sergey Ivanov
For a graph , its vertex deck is the multiset of graphs obtained by deleting one vertex. Bowler, Brown, and Fenner (BBF) proposed as the maximum possibl…
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…
Decision-making with possibilistic inferential models
Ryan Martin, Shih-Ni Prim, Jonathan Williams
Inferential models (IMs) are data-dependent, imprecise-probabilistic structures designed to quantify uncertainty about unknowns. As the name suggests, the focus has been on uncerta…
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…
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…
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…