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stat.ME2026

Enhanced localized conformal prediction with imperfect auxiliary information

Yinjie Min, Liuhua Peng, Changliang Zou

There is growing interest in constructing conformal prediction sets that provide approximate or asymptotic conditional coverage guarantees, capturing local data heterogeneity. Howe…

stat.ME2026

Efficient Federated Estimation and Inference for High-Dimensional Tail Index Regression

Haoyu Geng, Liuhua Peng, Changliang Zou +1

Tail index regression studies how covariates affect tail heaviness in heavy-tailed data. In many applications, data are distributed across heterogeneous sources, where direct pooli…

stat.ME2026

A Unified Theory of Conditional Coverage in Conformal Prediction with Applications

Yinjie Min, Liuhua Peng, Changliang Zou

Conformal prediction provides prediction sets with finite-sample marginal coverage, but many applications require coverage guarantees that adapt to individual test points, a subpop…

stat.ME2026

Stable Localized Conformal Prediction via Transduction

Yinjie Min, Liuhua Peng, Changliang Zou

Existing evaluations of conformal prediction, such as prediction efficiency and test-conditional coverage, are defined in expectation over the calibration data. In practice, when o…

stat.ME2025

Spatial-Sign based High dimensional Change Point Inference

Jixuan Liu, Long Feng, Liuhua Peng +1

High-dimensional changepoint inference, adaptable to diverse alternative scenarios, has attracted significant attention in recent years. In this paper, we propose an adaptive and r…