427 papers
This paper proposes a statistical framework to identify admissible allocation weights that achieve Pareto improvements without harming any participant, demonstrating its practical utility through an empirical analysis of resource reallocation in the RAND Health Insurance Experiment.
This paper proposes a conditional sign randomization method with estimated whitening for Gaussian kinship models that enables computationally efficient, finite-sample level-controlled gene-set association testing by leveraging sign-symmetry and reusable calibration across a sign orbit, while explicitly distinguishing its global null inference target from competitive enrichment or causal gene discovery.
This paper introduces Laplacian-P-splines (LPS) as a computationally efficient, sampling-free Bayesian alternative for estimating shared Gamma frailty models in clustered right-censored time-to-event data, demonstrating its performance through simulations and applications to three biomedical datasets.
This paper proposes and evaluates two enhanced multivariate generalized likelihood ratio (MGLR) control charts that integrate Klein's 2-of-2 and 2-of-3 sensitizing rules to effectively monitor simultaneous shifts in process mean and dispersion while explicitly accounting for the detrimental impact of measurement error.
This paper introduces a structural equation formulation for general quasi-periodic Gaussian processes that simplifies generation and forecasting while reducing computational complexity from to , offering a scalable and consistent approach for analyzing diverse natural and physiological signals.
This paper proposes a two-stage framework separating corpus qualification from Bayesian sequential stopping to demonstrate that even well-calibrated models and improved ranking cannot guarantee safe early termination in scholarly literature screening, as evidenced by high premature-stop rates and low recall in benchmark validations.
This study evaluates six spatio-temporal models for forecasting Indonesia's provincial Human Development Index, finding that simple local and cluster-adjusted approaches outperform complex deep learning architectures in short-term predictions, particularly when accounting for administrative boundary changes and limited training data.
This paper introduces the Global Benford Deviation Clustering (GBDC) framework, a novel unsupervised method that transforms global Benford's Law deviations into per-pixel anomaly scores to enable training-free radiometric anomaly detection in multispectral imagery, achieving a baseline mean F1-score of 0.347 on the CloudSEN12 dataset.
This study of over 100,000 students across 29 education systems reveals that distinguishing between parental screen-time rules that include versus exempt schoolwork uncovers significant differences in digital competence that are obscured by the traditional binary classification of rules as simply present or absent.
This paper introduces a statistically rigorous framework for testing structural balance in signed hypergraphs by defining a Hyperedge Frustration Index and a degree-preserving null model, which applied to UN General Assembly voting data reveals that genuine higher-order polarization exists only in specific historical windows rather than as a universal constant, distinguishing real coalitions from chance.