stat papers | Gist.Science

Matched-Calibration of Changepoint Detectors for Biopharmaceutical Quality Monitoring

This study demonstrates that when changepoint detectors for biopharmaceutical quality monitoring are compared under matched false-positive rates and search-space ablations, a standard variance-based penalty outperforms robust alternatives across various sample sizes and distributions, revealing that previous preferences for robust methods were driven by uncontrolled experimental conditions rather than inherent superiority.

Halil İbrahim Özdemir, Berfin Dogan, Pemra Ozbek2026-08-14

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Choosing Among Randomized Design Families for Implementation Strategy Trials: A Question-Driven Framework with Practical Tools and Exemplars

This paper presents a practical, question-driven framework and accompanying tools to guide researchers in selecting the most appropriate randomized design family—such as parallel cluster trials, stepped-wedge, factorial, or adaptive trials—for implementation strategy studies based on their specific decision-making goals rather than methodological preference.

Jonah K. Amponsah, Shellie D. Ellis, Felice Resnik2026-08-14

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Certifying long-term statistical fidelity of learned chaotic surrogates without rolling them out

This paper demonstrates that one-step prediction error is a poor predictor of long-term statistical fidelity in learned chaotic surrogates and introduces a system-specific response operator that can efficiently bound long-run statistical errors without requiring computationally expensive rollouts.

Anqi Pan2026-08-14

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Robust Log-Contrast Regression for High-Dimensional Compositional Microbiome Data with Outliers

This paper proposes a robust log-contrast regression framework that combines Huber loss and Lasso penalty to handle outliers and ensure sparsity in high-dimensional compositional microbiome data, while providing theoretical guarantees and an efficient symmetric Gauss-Seidel ADMM algorithm for implementation.

Xuke Hua, Yunhai Xiao, Xin Xin2026-08-14

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Manual versus Data-Driven Specification in Hybrid Discrete Choice Models} - A Benchmark of MNL, RUMBoost, and Penalized Multinomial Logistic Tree Regression (PMLTR)

This paper introduces Penalized Multinomial Logistic Tree Regression (PMLTR) as a hybrid model that balances the predictive power of data-driven methods like RUMBoost with the interpretability of classical MNL models, demonstrating through extensive benchmarking that while manual specification remains competitive, purely data-driven approaches excel in prediction whereas PMLTR uniquely offers readable utilities essential for optimization and inference.

Simon Rienks, Fiona Sauerbier, Knut Haase2026-08-14

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Multi-Perspective Ensemble Stratification-Driven Quality of Life Assessment for Transformative Planning of Coastal Communities

This study proposes a multi-perspective ensemble stratification framework that integrates heterogeneous data and diverse clustering models to comprehensively characterize household vulnerability and identify key drivers of quality of life in coastal Nigerian communities, thereby enabling more effective, evidence-based transformative planning and resource allocation aligned with sustainable development goals.

Taiwo Olapeju Olaleye, Taiwo Ajayi, Oluwasegun Dada, Temitope Aderele, Segun Akintunde, Oluwasefunmi Arogundade2026-08-14

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Distribution Analysis of Discrete States: reconstructing transition processes from time-resolved state distributions

This paper introduces Distribution Analysis of Discrete States (DADS), a general framework that reconstructs underlying transition processes from observed state distributions by modeling them as flow networks, establishing a rigorous typology of non-identifiability, and validating the approach through seven diverse case studies and self-correcting diagnostic tests.

Thomas Potempa2026-08-14

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Q-Multi-Criteria Consensus Model: Synthesizing cognitive priority structures from multi-actor disagreement

This paper introduces the Q-Multi-Criteria Consensus Model (Q-MCCM), a hybrid framework that synthesizes subjective disagreements into shared priority structures by combining Q-methodology with an algorithmic Best–Worst Method, thereby enabling robust institutional decision-making in contexts where priorities must be negotiated rather than alternatives selected.

Marek Deja2026-08-14

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An outlier-robust dynamic semiparametric model for jointly forecasting value-at-risk and expected shortfall

This paper proposes an outlier-robust joint dynamic semiparametric model that integrates bounded innovation propagation into the CAViaR framework to improve the accuracy and reliability of Value-at-Risk and Expected Shortfall forecasts, particularly in the presence of extreme observations and heavy-tailed distributions.

Jie Wang, Buyun Xu2026-08-14

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The Role of Statistics in the Advancement of Artificial Intelligence and Modern Technology

This study utilizes machine learning time series modeling and correlation analysis of publication data from 1991 to 2023 to demonstrate that statistics serves as a central, tightly coupled driver of Artificial Intelligence and modern technology advancement, with both fields exhibiting strong positive growth trends and a statistically significant interdependence projected to continue through 2030.

H. C. Iwu, D. C. Bartholomew, C. J. Ijemba2026-08-14

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