arXiv subjects

Liangliang Yuan

Publications and source records attributed to Liangliang Yuan.

3 recordsLinked to original sources

arXiv2026

Rank-based Maxsum test for high dimensional regression coefficient

We study global inference for regression coefficients in high-dimensional linear models under potentially heavy-tailed errors. While sum-type tests are powerful for dense alternatives and max-type tests excel for sparse alternatives, practical applications rarely reveal the sparsity level, and many existing procedures rely on light-tail assumptions. Motivated by the Wilcoxon-score sum test of Feng et al. (2013) and the two Wilcoxon-score maximum tests of Xu and Zhou (2021), we establish under $H_0$ the asymptotic independence between the rank-based sum statistic and each max statistic. These joint limit results justify principled $p$-value aggregation, and we propose two adaptive rank-based maxsum tests via the Cauchy combination method (Liu and Xie, 2020). The proposed procedures inherit robustness from rank-based construction and adaptivity from combining dense- and sparse-sensitive components. Simulation studies confirm accurate size control and strong power across a wide range of error distributions and sparsity regimes.

stat.ME↗

arXiv2026

Conformalized Robust Principal Component Analysis

Robust principal component analysis (RPCA) is a widely used technique for recovering low-rank structure from matrices with missing entries and sparse, possibly large-magnitude corruptions. Although numerous algorithms achieve accurate point estimation, they offer little guidance on the uncertainty of recovered entries, limiting their reliability in practice. In this paper, we propose conformal prediction-RPCA (CP-RPCA), a practical and distribution-free framework for uncertainty quantification in robust matrix recovery. Our proposed method supports both split and full conformal implementations and incorporates weighted calibration to handle heterogeneous observation probabilities. We provide theoretical guarantees for finite-sample coverage and demonstrate through extensive simulations that CP-RPCA delivers reliable uncertainty quantification under severe outliers, missing data and model misspecification. Empirical results show that CP-RPCA can produce informative intervals and remain competitive in efficiency when the RPCA model is well specified, making it a scalable and robust tool for uncertainty-aware matrix analysis.

stat.ME↗

arXiv2025

Ionospheric Electron Heat Flow Modulates Planetary Ambipolar Electric Fields

The ambipolar electrostatic field has long been recognized as a key driver of ion escape from planetary atmospheres. Elucidating the mechanisms responsible for the generation of this field is critical for understanding atmospheric escape and the evolution of habitability on terrestrial planets. Yet, existing comparisons between ambipolar diffusion theory and in-situ potential measurements have largely neglected the effect of electron heat flow. Confronting the theory incorporating heat-flow effect with in-situ electrical potential data from the \textit{Endurance} sounding rocket mission, we identify observational signatures of electron heat-flow effects. Furthermore, the implications of electron heat-flow effect across terrestrial planets are revealed, focusing on its capacity to resolve the enigma of Venusian electric potential drop anomaly. The anisotropic ion temperatures and the associated enhancement of electron heat-flow effect can explain the anomalous electric potential drop observed in the ionosphere of Venus.

astro-ph.EP↗