Conformalized Regression for Continuous Bounded Outcomes
This work develops Conformalized Regression for bounded outcomes, creating prediction intervals for models like beta regression and logit-normal regression where results are constrained (e.g., proportions). It introduces quantile residuals as a score to handle model complexity and boundary effects, providing validity guarantees for these common models.