421 papers
This paper introduces a total-variation certification framework to evaluate approximate samplers for covariate-constrained cluster randomization, demonstrating on a fixed benchmark and synthetic extensions that current quantum and classical approximations often deviate significantly from exact targets, thereby inflating type-I error rates and altering p-values in randomization tests.
This paper proposes a flexible, computationally efficient framework for population mode estimation using penalized, data-driven B-splines to model the log-density, demonstrating superior performance over traditional kernel and fixed-knot spline methods through theoretical analysis, simulations, and real-world applications.
This paper proposes a Bayesian population-aware fairness framework that integrates complex survey weighting, hierarchical partial pooling, and uncertainty calibration to accurately estimate and certify group disparities in predictive systems, demonstrating through simulations and real-world data that ignoring survey design leads to biased fairness conclusions.
This paper proposes an adaptive functional principal component forecasting framework in Wasserstein space for cross-sectional ETF return distributions, demonstrating that combining a strong FPCA autoregressive anchor with either selected-quantile residual correction or global score dynamics yields modest but stable improvements over standard and unstable alternatives.
This methodological study develops and validates a flexible cleaning algorithm to standardize CMS Medicaid Managed Care Enrollment Report data, thereby enabling more accurate longitudinal tracking of market consolidation trends across 11 U.S. states from 2016 to 2021.
This paper introduces and validates an Adaptive Mortality Drift (AMD) model that outperforms various statistical and machine learning benchmarks in forecasting short-term under-five mortality trends across 16 West African countries by dynamically estimating mortality persistence and demonstrating robustness through Monte Carlo simulations.
This empirical study of FDA MAUDE data demonstrates that broad-period positive disproportionality signals are often methodologically unstable, necessitating temporal, entity-label, and comparator stress tests to distinguish robust reporting patterns from artifacts before drawing conclusions about device safety.
This paper proposes a stochastic framework using closed Markov and open semi-Markov population models to analyze infectious disease transmission in interconnected compartmental spaces by accounting for spatial structure, individual mobility, and general residence-time distributions.
This Monte Carlo study demonstrates that while AI-assisted writing improvements do not automatically preserve the relative proficiency rankings of learners, the resulting rank reversals and selection instability vary significantly depending on the assistance mechanism, with weakness-targeted gains causing substantial ordering errors and selection accuracy declines that cannot be fully corrected by product-only score calibration.
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.