The paper introduces two new methods for conformal prediction when training data are coarsened or partially missing. Robust Split Prediction (RSP) extends doubly robust estimating equations to general coarsening patterns, producing multiply robust prediction sets. Robust Split Conformal Prediction (RSCP) combines semiparametric theory with conformal risk control by fixing a preliminary threshold estimate to restore monotonicity, enabling higher‑order bias‑controlled coverage guarantees. The authors provide theoretical results for various missingness scenarios and demonstrate the methods with simulations and a rheumatoid‑arthritis study.
arXiv:2607.16675v2 Announce Type: replace Abstract: A predictor with small overall bias can still exhibit systematic bias conditional on its predicted value, limiting its reliability for downstream u...
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
arXiv:2606. 31600v1 Announce Type: cross Abstract: Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarantees.
arXiv:2507. 14023v2 Announce Type: replace-cross Abstract: Regression problems with bounded continuous outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions.
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
arXiv:2603.23923v2 Announce Type: replace-cross Abstract: Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution an...
arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.
arXiv:2607. 04236v1 Announce Type: cross Abstract: Bayes-assisted conformal prediction combines the strengths of Bayesian modelling with exact, distribution-free frequentist coverage guarantees.
arXiv:2607. 03161v1 Announce Type: cross Abstract: In selective deployment, practitioners act only on a model-chosen subset of individuals based on predicted conditional average treatment effects, but marginal conformal guarantees need not control reliability on that selected subset.
arXiv:2606.11136v3 Announce Type: replace-cross Abstract: We develop a framework for conformal prediction in dyadic regression problems under complex missingness mechanisms. At the theoretical level,...
arXiv:2601. 13102v3 Announce Type: replace-cross Abstract: Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estimators.
arXiv:2604.22391v2 Announce Type: replace-cross Abstract: The Super Learner (SL) is a widely used ensemble method that combines point predictions from a library of learners based on their predictive...