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Journal of Agricultural, Biological, and Environmental StatisticsA Likelihood Framework for Multi-Stage Interval-Censored Processes

This paper proposes a rigorous likelihood-based framework utilizing a Monte Carlo Expectation-Maximization algorithm to model multi-stage biological processes with interval-censored transitions and right-censored disease events under time-varying environmental forcing, addressing the computational challenges of high-dimensional integrals and non-proportional hazards to enable precise statistical inference for agro-environmental data.

Patrice Takam Soh, Tsogmo Douane Cybele, Toussile Wilson2026-10-14

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Tail-Accuracy Degradation and Optimal Block Length for Block Self-Normalized Quantile Empirical Saddlepoint Inference under Dependence

This paper quantifies the tail-accuracy degradation of block self-normalized quantile empirical saddlepoint inference under strong mixing dependence and derives a closed-form optimal block length scaling as $\sqrt{n}$ that balances a non-vanishing variance-inflation floor with skewness transients to achieve $O(n^{-1})$ coverage error.

Yucheng Tao2026-10-14

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Is Using Shapley Value Good for Covariate Selection in Panel Models?

This paper proposes and validates a fixed-effects Shapley-value method for covariate selection in panel models, demonstrating through simulations and empirical applications that it offers a transparent, computationally efficient, and parsimonious alternative to existing approaches while accurately recovering true active sets and improving model performance.

Htwe Ko, Pathairat Pastpipatkul2026-10-14

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Horizon-resolved Forecastability of Time Series via Auto Mutual Information

This paper demonstrates that auto-mutual information (AMI) serves as an effective pre-modelling diagnostic for assessing time series forecastability across specific horizons, revealing a systematic gradient where higher AMI values correlate with significantly lower forecasting errors compared to absolute autocorrelation.

Peter Maurice Catt2026-10-13

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Attractor strength identifies classifier label geometry as construct- irrelevant variance in machine-scored developmental drawing assessment

This study demonstrates that in machine-scored developmental drawing assessments, the perceived difficulty of drawing specific objects is largely driven by construct-irrelevant variance stemming from the scoring pipeline's classifier label geometry rather than the child's actual developmental ability, thereby rendering single-prompt scores unreliable and necessitating attractor-adjusted metrics for valid cross-pipeline comparisons.

Rafat Saleh Madani2026-10-13

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Anchoring on Target: Rethinking Weighting Logic and Paradigm Reconstruction

This paper critiques the paradigm bias in traditional indicator-driven weighting methods for conflating statistical characteristics with indicator importance and proposes the Target-Oriented Weighting (TOW) method, which anchors weights to evaluation objectives through benchmark-based calculations and a Difference-in-Differences-inspired logic to assess improvement magnitude rather than static levels.

Yongliang Zhao, Liping Yu2026-10-13

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A Comprehensive Reliability Assessment of the Kumaraswamy Inverse Weibull Distribution with Application to Electromigration Failure-Time Data

This paper presents a comprehensive reliability assessment of the Kumaraswamy Inverse Weibull distribution, demonstrating through structural analysis, maximum likelihood estimation, and comparative goodness-of-fit tests that it effectively models electromigration failure-time data in microcircuits, outperforming or competing with several alternative inverse-Weibull-type distributions.

Muhammad Shuaib Khan2026-10-13

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Transient Probabilities and Switching Levels in a Vacation Queue with Catastrophes

This paper analyzes a discrete-time single-server queue with vacations and catastrophes by employing a two-color Motzkin path representation to derive transient and stationary probabilities, establish a composition scheme for service-return cycles, and identify distinct relaxation factors and critical scaling behaviors.

Igor Kleiner2026-10-13

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Large-Scale Group Decision-Making with Linear Uncertain Preference Relations Using Improved Density Peak Clustering and Two-Stage Feedback

This paper proposes a large-scale group decision-making model for linear uncertain preference relations that integrates dynamic trust updating, improved Density Peak Clustering for subgroup identification, and a two-stage feedback mechanism to achieve high consensus rates with minimal preference adjustments.

Xiaoyun Lu, Kuiyao Liu, Fushan Li, Zhonglin Ye, Zehui Chen2026-10-13

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A Bayesian Alternative to the Two One-Sided Tests (TOST) Procedure: State of the Art and a Proposal for a Generalized Bayesian Equivalence Model

This paper proposes the Generalized Bayesian TOST with Power-Discounted Priors (GBT-PP), a unified framework that overcomes the limitations of the classical TOST procedure by formally incorporating and tunably discounting historical data from both continuous and count sources, while establishing five original theoretical results and providing reproducible R implementations for equivalence testing.

Antonio Monleon-Getino2026-10-13

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