stat papers | Gist.Science

Stability and Interrelationships of Classical Test Theory Indicators Under Varying Difficulty Conditions: A Monte Carlo Simulation Study

This Monte Carlo simulation study demonstrates that under structural independence, Classical Test Theory item-level indicators exhibit mathematically determined relationships—such as the perfect functional equivalence of binary item statistics and the stability of discrimination-total correlations across difficulty levels—while test-level reliability remains low due to the absence of inter-item covariance.

Ammar Yasser2026-07-02

📊 statistics

Logistics-driven proactive drone epidemic intervention model

This paper proposes a logistics-driven proactive epidemic intervention model that integrates an RM-SIR mathematical framework with an intelligent drone delivery system to enable precise, rural-prioritized resource scheduling, effectively transforming logistics from a passive transport function into an active epidemic control tool that significantly reduces outbreak peaks and duration while maintaining high cost-effectiveness.

Jiaen Zheng2026-07-02

📊 statistics

Statistical Advances in Nonparametric Estimation through Artificial Intelligence Techniques

This paper surveys the theoretical and practical integration of artificial intelligence techniques, such as deep learning and reinforcement learning, into nonparametric estimation to address high-dimensional challenges and complex dependencies in fields like medicine and economics, while critically examining trade-offs between interpretability, computational cost, and statistical guarantees.

Sthitadhi Das2026-07-02

📊 statistics

Accurate mean-field predictions for cognitively grounded social influence dynamics with confirmation bias

This paper demonstrates that cognitively detailed agent-based models of social influence with confirmation bias can be accurately reduced to a low-dimensional, analytically tractable mean-field system that captures the transition from consensus to polarization and provides a simple stability test for diagnosing symmetry breaking.

Sven Banisch, Joris Wessels2026-07-02

📊 statistics

Trust in Health Information Sources and Perceived Climate Change Health Risks: Evidence from the 2022 and 2024 Health Information National Trends Survey

Using data from the 2022 and 2024 Health Information National Trends Survey, this study reveals that trust in government health agencies is the strongest predictor of perceiving climate change as a health risk, while trust in scientists shows a weaker, diminishing association over time and trust in doctors is not a significant factor.

Miyeon Kim2026-07-01

📊 statistics

Mathematical Modeling of the Rate of Cognitive Adaptation With an AI Tutor Among Adult Learners

This empirical study validates a mathematical model demonstrating that adult learners' rate of cognitive adaptation in technical fields is primarily driven by the ratio of intellectual humility to ego-noise, rather than initial intellectual potential, with minimizing ego-noise significantly accelerating the time to reach a plateau of correct solutions.

Kibriyo Mukhamadieva2026-07-01

📊 statistics

Scalable Random Survival Forests via Risk Set Sampling

This paper proposes a scalable Random Survival Forests method that utilizes risk-set sampling to significantly reduce training time while maintaining or even improving predictive performance, with the approach now implemented in the widely used R package `ranger`.

Justine B. Nasejje, Niklas Koenen, Marvin N. Wright, Henry Mwambi2026-07-01

📊 statistics

A New Pareto-Type Family via the Right-Sided Riemann–Liouville Fractional Integral: Properties and Applications

This paper introduces a new flexible family of Pareto-type distributions generated via the right-sided Riemann–Liouville fractional integral operator, which offers a theoretically grounded mechanism for regulating tail behavior and demonstrates superior performance in modeling heavy-tailed real-world data compared to existing models.

Kamaldeen Olomoda ISIAK, AKEYEDE Imam, Yunus MUSA Olatunji, OYETAYO Oyebisi, Muhammad Abiodun SULAIMAN2026-07-01

📊 statistics

Real-world performance of large-scale propensity score adjustment strategies: Matching, weighting, and stratification

This study evaluates large-scale propensity score adjustment strategies across four national healthcare databases and concludes that inverse probability of treatment weighting with Crump trimming and 1:1 matching generally offer the best performance, though no single strategy is universally superior, necessitating the use of diagnostics and empirical calibration to guide selection.

Kelly M Li, Martijn J Schuemie, Patrick B Ryan, Linying Zhang, Yong Chen, Kashish Priyam, Nicole Pratt, George Hripcsak (…)2026-07-01

📊 statistics

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

← Previous Next →