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A Probabilistic Framework for Reconstructing Sparse UAV-Based Radiation Monitoring Data Using Gaussian Process Regression and Uncertainty Quantification

This study proposes an uncertainty-aware probabilistic framework that utilizes Gaussian Process Regression to effectively reconstruct sparse UAV-based radiation monitoring data from the Chornobyl Exclusion Zone, outperforming traditional interpolation methods in accuracy while providing essential spatial uncertainty estimates for informed environmental decision-making.

Andrii Bondarchuk, Tetiana Nosenko, Yurii Zabulonov2026-06-30

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Robust Regularised M-Estimators for High-Dimensional Regression with Heavy-Tailed and Skewed Errors

This paper introduces a class of robust regularised M-estimators for high-dimensional regression that achieves optimal convergence rates and variable selection consistency under minimal moment conditions (finite $\alpha > 1$ ), outperforming existing methods in both simulation and real-world genomic applications when dealing with heavy-tailed, skewed, or contaminated errors.

Mazona Victor, Vincent Odiaka, Gabriel O. Obadina2026-06-30✓ Author reviewed ⓘ

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Sparse Convex Biclustering

The paper proposes Sparse Convex Biclustering (SpaCoBi), a novel convex optimization method with a stability-based tuning criterion that effectively addresses noise and computational challenges in high-dimensional datasets, demonstrating superior accuracy and robustness compared to existing state-of-the-art biclustering techniques.

Jiakun Jiang, Dewei Xiang, Cheliang Gu, Wei Liu, Binhuan Wang2026-06-30

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Detecting and Quantifying Circularity Bias When Composite Quality Ratings Are Used as Covariates: A Methodological Demonstration Using CMS Hospital Excess Readmissions Data

This paper demonstrates that including composite quality ratings as covariates in regression models introduces a circularity bias that systematically attenuates the estimated effects of structural predictors, a methodological hazard confirmed through CMS hospital readmission data and Monte Carlo simulations.

Ali Akram2026-06-30

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Perceived scarcity and risky choice under high objective risk: Evidence from a hierarchical drift diffusion model

This study demonstrates that perceived resource scarcity increases risky decision-making, particularly under high objective risk, by accelerating the drift rate toward the risky option as revealed through hierarchical drift diffusion modeling.

Hongmin Luo, Junyu Liang, Jun Chen2026-06-30

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Explainable Medicare Payment Integrity Analytics using Rank-Aggregated Anomaly Ensembles and Conformal Cost Reference Bands

This paper presents an explainable, reproducible public-data framework for Medicare payment integrity that integrates rank-aggregated anomaly ensembles and conformal cost-reference bands to prioritize audit triage while distinguishing statistical anomalies from proof of misconduct.

Nassamon Bootwisas, Pasin Marupanthorn2026-06-29

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A median-based estimate of effect size - toward more intuitive comparisons in mathematics education research

This paper introduces a median-based effect size indicator for mathematics education research that offers a more intuitive and robust alternative to Cohen's d by comparing group values to medians, thereby reducing sensitivity to skewed distributions, outliers, and small sample sizes while better aligning with practical interpretations of student performance.

Gary Davis, Mercedes McGowen, Sara Dalton Bildik, Donghui Yan2026-06-29

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A Hybrid Machine Learning–Bayesian Framework for Correcting Measurement Error in Health Data

This paper introduces the HAIB-MEC framework, a hybrid machine learning–Bayesian system that integrates nonlinear predictive modeling with hierarchical uncertainty correction to significantly improve accuracy, reduce bias, and enhance reliability in health datasets affected by measurement error.

Romuald Daniel BOY-NGBOGBELE2026-06-29

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The Nonsense Dependence Index: A Rank-Based Diagnostic for Identifying Potentially Spurious Associations

This paper introduces the Nonsense Dependence Index (NDI), a simple rank-based diagnostic defined as the difference between Kendall's $\tau$ and the square root of Bergsma–Dassios' $\tau^*$ , to effectively distinguish genuine monotone relationships from spurious associations driven by common trends or external factors.

STHITADHI DAS2026-06-29

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Comparative Study of Sarima and Lstm Model to Forecast the Insurance Comprehensive Income (Loss) in Indonesia

This study compares SARIMA and LSTM models for forecasting Indonesian insurance comprehensive income, finding that while SARIMA better captures seasonal trends, both models exhibit limited accuracy, suggesting that future hybrid approaches and external variables are needed to improve forecasting performance.

Hiteash Mahajan2026-06-29

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