422 papers
This paper establishes that in Gaussian in-context regression with one labeled observation, the statistical cost of estimating unknown task geometry is distinct from and strictly positive compared to the cost of selecting a geometric action without label feedback, revealing that label feedback provides a unique advantage in reducing Bayes approximation regret even as total risk decreases with more reference data.
This study demonstrates that Bayesian small-area estimation models leveraging Demographic and Health Surveys and census data can generate robust, fine-scale, and temporally consistent socioeconomic estimates for sparse and heterogeneous regions like the Peruvian Amazon, thereby revealing critical spatial links between poverty, education, and malaria burden that are often obscured in traditional broad-scale analyses.
This paper introduces and validates two novel screening methods, Rare-Screening and Firth-ttScreening, designed to improve the detection of CpG sites associated with rare binary events in high-dimensional epigenome-wide association studies, demonstrating superior sensitivity compared to traditional FDR and Bonferroni approaches.
This study demonstrates that for drinking-water compliance classification, machine learning models cannot outperform a regulatory rule-based baseline because domain-defined thresholds already capture nearly all available signal, highlighting that refining regulatory rules and implementing consensus strategies are more effective than model selection.
This paper presents and validates an eight-stage open pipeline for mediation analysis on multi-edition city indices, demonstrating through extensive simulations that while standard methods suffer from inflated Type I errors and severe bias due to unobserved or time-varying confounding, a combination of city-cluster bootstrapping, two-way fixed-effects models, and residual-correlation sensitivity analysis provides a robust protocol for reliable governance mediation claims.
This paper introduces the Synergy Framework, a four-view, domain-self-calibrated instrument that integrates structural coupling, multibody organization, dynamical stability, and perturbation recovery to measure complex systems while explicitly reporting failure modes and avoiding single-view dependencies.
This paper demonstrates that spatially correlated measurement error amplifies attenuation bias and invalidates standard instrumental variables, proposing a higher-order spatial lag GMM estimator that leverages geometric mixing conditions to achieve significant bias reduction and valid inference, as validated by simulations and a Tunisian infrastructure application.
This paper proposes and evaluates classical and Bayesian methods for estimating Weibull distribution parameters under fuzzy data using alpha-cuts, demonstrating through simulation and real-world application that these approaches yield competitive accuracy and reveal unique behaviors in shape parameter estimation compared to crisp-data methods.
This study employs explainable machine learning and panel data from 161 countries to demonstrate that national life satisfaction and suicide mortality often diverge due to factors like unemployment and alcohol consumption, revealing specific high-risk country profiles while cautioning against conflating aggregate statistical patterns with individual causal mechanisms.
This paper introduces the Anthropometric Relationship Matrix (ARM) and the Anthropometric Structural Stability Index (ASSI) as a novel, structurally complete framework for representing and statistically inferring the proportional organization of positive ratio-scale systems, offering a complementary approach to traditional multivariate methods with broad applications in fields ranging from anthropometry to engineering.