Conditional Testing based on Localized Conformal p-values
Abstract
In this paper, we address conditional testing problems through the conformal inference framework. We define the localized conformal p-values by inverting prediction intervals and prove their theoretical properties. These defined p-values are then applied to several conditional testing problems to illustrate their practicality. Firstly, we propose a conditional outlier detection procedure to test for outliers in the conditional distribution with finite-sample false discovery rate (FDR) control. We also introduce a novel conditional label screening problem with the goal of screening multivariate response variables and propose a screening procedure to control the family-wise error rate (FWER). Finally, we consider the two-sample conditional distribution test and define a weighted U-statistic through the aggregation of localized p-values. Numerical simulations and real-data examples validate the superior performance of our proposed strategies.
Cite
@article{arxiv.2409.16829,
title = {Conditional Testing based on Localized Conformal p-values},
author = {Xiaoyang Wu and Lin Lu and Zhaojun Wang and Changliang Zou},
journal= {arXiv preprint arXiv:2409.16829},
year = {2024}
}