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425 papers

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Subgroup AUROC-gap tests are not evidence of unfairness in clinical prediction models: case-mix differences make the equal-discrimination null false

This paper demonstrates that standard subgroup AUROC-gap tests are invalid for assessing fairness in clinical prediction models because differences in case-mix distributions naturally produce AUROC disparities even in perfectly fair models, whereas calibration tests remain robust for distinguishing true unfairness from geometric artifacts.

Rohith Reddy Bellibatlu, Manpreet Singh, Yash Jajoo, Abhishek Israni, Rahul Joshi2026-10-06
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When does validation design change the answer? A simulation study and two empirical applications in clustered longitudinal health data

This study demonstrates that while within-cluster correlation is necessary for row-level validation leakage, cluster size acts as the binding constraint in real-world data, suggesting that researchers should prioritize temporal validation and use pre-modelling diagnostics to determine when stricter validation designs are truly required.

HanYeol Nam, Jin Yoo, Doo-Hee Lee, Jeongyun Kim, Yoon Jeong Jang, Ku Kang2026-10-06
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Independence and Collaboration in Single-Case Evidence Standards: Inductive Themes from Two Expert Focus Groups

Through inductive thematic analysis of two expert focus groups, this paper proposes a staged workflow and transparency infrastructure to reconcile the tension between collaborative multi-site development and the independence requirements of single-case evidence standards, thereby enhancing replicability without stifling coordinated research.

Joseph M. Lambert, Mandy J. Rispoli, Jennifer R. Ledford2026-10-06
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A Distribution-Based Framework for Homogeneity Assessment of Engineering Data: Comparison with Conventional Statistical Inference

This paper proposes a distribution-based framework using Interval-Shift Density Estimation (ISDE) to assess the homogeneity of engineering data as production system outputs, demonstrating that this assumption-free approach yields more stable and reliable results than conventional statistical inference methods as data volume increases.

Yu Yun, Weigang Duan2026-10-06
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Three inference traps in exploratory space–time analysis: evidence from population ageing in Türkiye

This paper identifies and provides diagnostics for three common inference traps in exploratory space–time analysis—specifically regarding the misinterpretation of local statistics, the conflation of temporal trends with spatial clustering in hot spot analysis, and the misattribution of class-position effects to spatial contagion—demonstrating through Türkiye's population ageing data that observed spatial patterns stem from shared regional forcing rather than neighbor-to-neighbor diffusion.

Kerem Yavuz ARSLANLI2026-10-06
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A Systematic Review of Zero-Inflated Count Time Series Models: Techniques, Developments, Challenges and Future Directions

This paper presents the first comprehensive systematic literature review and bibliometric analysis of Zero-Inflated INAR count time series models, synthesizing methodological developments, estimation techniques, and applications while identifying persistent challenges like overdispersion and computational burden to guide future research directions.

Saurabh Kumar, Suraj Prakash2026-10-06
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Weibull-MBUR {Y} A New Family of Generalized Unit Distributions with Correlated Parameters: Is the Correlation, Distribution or Data Driven or Interaction Between Them

This paper introduces the Weibull-MBUR {Y} family of generalized unit distributions by applying the T-X{Y} framework to link a baseline Median Based Unit Rayleigh distribution with a Weibull generator through various link functions, deriving their statistical properties and analyzing their parameter correlations using real-world COVID-19 recovery data to determine if such correlations are driven by the data, the distribution, or their interaction.

iman attia2026-10-06✓ Author reviewed ⓘ
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Broadly Applicable Approximate MCMC for Switching Stochastic Differential Equations Using Uniformization and Time-Conditioned Factorized Neural Likelihood Estimation

This paper proposes a broadly applicable approximate Markov chain Monte Carlo sampler for switching stochastic differential equations that combines uniformization with time-conditioned factorized neural likelihood estimation to overcome the limitations of existing methods by enabling Bayesian inference without requiring analytically tractable transition densities.

Shion Hosoda, Michiaki Hamada2026-10-06
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Before Nonresponse: School-Relative Response Timing and Selective Observability in PISA 2025 Context Questionnaires in Latin America

This study of PISA 2025 data from Latin America reveals that late questionnaire nonresponse is often preceded by sustained rapid responding, a transition linked to school-level socioeconomic factors and peer timing, which leads to selective observability that slightly biases regional study-planning estimates but highlights the value of response timing data for distinguishing response quality from missingness.

Jorge Eliécer Villarreal-Fernández2026-10-06