stat papers | Gist.Science

Hybrid Neutrosophic Regression ANOVA (HNRA): An Uncertainty-Aware Statistical Inference Framework Under Measurement Imprecision

This paper introduces Hybrid Neutrosophic Regression ANOVA (HNRA), a novel statistical framework that models measurement imprecision as neutrosophic intervals to provide a bounded, uncertainty-aware decomposition of sums of squares and a formally guaranteed F-statistic, thereby extending classical regression ANOVA to scenarios with instrument calibration errors and biological variability without requiring prior distributional assumptions.

Jaida Najihah Jamidin, Zahari Md Ro, Sayang Mohd Deni, Siti Meriam Zahari2026-07-23

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Analysis of Italian mobility patterns using institutional and networked data

This paper evaluates the utility of web-scraped BlaBlaCar data as a complementary source to traditional ISTAT census statistics for analyzing Italian mobility patterns, demonstrating how combining institutional and networked data with gravity models can capture both routine commuting and voluntary long-distance travel trends.

Mauro Mazzei, Teresa Bartolomei2026-07-23

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A Mathematical Model of Swine Flu (H1N1) Transmission with Imperfect Behavioral Vaccination Dynamics: A Case Study of Japan

This study proposes a novel eight-compartment deterministic model incorporating time-dependent behavioral vaccination dynamics to analyze H1N1 transmission in Japan, demonstrating that the basic reproduction number governs disease stability and that effective mitigation relies on optimizing vaccination coverage, quarantine measures, and reducing perceived vaccination costs.

Md. Zakir Hosen, Akhil Kumar Srivastav, Jun Tanimoto2026-07-21

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Value of Information under Imprecise Probabilities

This paper proposes a generalized Value of Information (IP-VOI) framework that extends conventional analysis to settings with imprecise probabilities by evaluating decision stability across an admissible set of probability measures, thereby distinguishing robust research conclusions from those dependent on specific modeling assumptions.

Rowan Iskandar2026-07-20

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Probabilistic Risk Stratification and Transition-Sensitive Explainability for Attorney Involvement in Workers' Compensation Claims

This study utilizes a CatBoost classifier on 48,130 workers' compensation claims to model attorney involvement as a probabilistic risk process, demonstrating how litigation drivers shift across low-, medium-, and high-risk tiers and revealing asymmetric feature patterns near regime boundaries to enhance risk stratification and explainability.

Gonzalo Agustin Vivian, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar2026-07-20

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Reverse-Engineering Radiation Oncology: A Reproducible Pipeline for Affiliation Disambiguation and Author Localisation in the Medical Sciences

This paper presents a reproducible, field-agnostic pipeline that improves the accuracy of author location and seniority assignment in radiation oncology by deriving data directly from raw affiliation strings rather than relying on error-prone institutional tags, achieving significantly higher precision (F1 ≈ 0.92) than standard baselines.

David Kaul2026-07-20

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Women’s knowledge and attitude scale toward ai-based chatbot systems in preconception counseling: A scale development study

This study developed and validated a reliable 19-item, three-factor scale to effectively assess women's knowledge, attitudes, acceptance, and trust regarding AI-based chatbot systems in the context of preconception counseling.

Ebru Cirban Ekrem, Güleser Ada2026-07-20

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The name-collision burden of name-based author attribution is unequal across name origins: a measurement in OpenAlex, and an identifier-based remedy (SigmaCV)

This study quantifies the significantly higher burden of name collisions faced by East-Asian researchers compared to their Anglophone and Other counterparts within the OpenAlex database, demonstrating that name-based attribution is inherently unequal and advocating for the adoption of identifier-based solutions like SigmaCV to ensure fair research assessment.

Basile Chrétien2026-07-17

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Robust Dependence Detection in Official Statistics: A Data Based Methodology

This paper proposes Bergsma-Dassios's τ∗ as a robust, sign-covariance-based dependence measure for official statistics, demonstrating through theoretical analysis, EU-SILC data applications, and simulation studies that it outperforms traditional correlation metrics in detecting nonlinear relationships and handling data contamination.

STHITADHI DAS2026-07-17

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Quantile Necessary Condition Analysis: A Tolerance-Parameterized Generalization of Necessary Condition Analysis

This paper introduces Quantile Necessary Condition Analysis (QNCA), a tolerance-parameterized extension of NCA that replaces the deterministic ceiling with conditional-quantile frontiers to mitigate the sensitivity of effect sizes to single boundary observations while preserving the method's core interpretive framework.

Marek Deja2026-07-15