A0844
Title: Conformal prediction for Dyadic regression
Authors: Robert Lunde - Washington University in St Louis (United States) [presenting]
Liza Levina - University of Michigan (United States)
Ji Zhu - University of Michigan (United States)
Abstract: Dyadic regression, which involves modeling a relational matrix given covariate information, is an important task in statistical network analysis. Uncertainty quantification is considered for dyadic regression models using conformal prediction. Novel non-conformity scores are proposed for this setting, and finite-sample validity is established in the procedures for various sampling mechanisms under a joint exchangeability assumption. It is also shown that, under certain conditions, it is possible to construct asymptotically valid prediction intervals for a missing entry under a structured missingness assumption.
