252 papers
This paper introduces a two-dimensional propensity score matching (2D-PSM) method that separately estimates scores for categorical and continuous confounders to achieve superior multivariate balance in observational studies with mixed covariate types and complex interactions compared to traditional single-score approaches.
This paper provides a structured operations research tutorial that operationalizes and compares various fairness metrics, allocation schemes, and efficiency-fairness trade-offs within homogeneous resource settings, using a cloud computing proof-of-concept to illustrate the properties and strategic vulnerabilities of different fair-division paradigms.
This study develops a high-resolution, non-compensatory geospatial-statistical index of multidimensional access to inpatient paediatric emergency care in Kenya that reveals significant disparities masked by traditional travel-time metrics and demonstrates a stronger protective association with child survival, offering a superior framework for targeting health investments.
This paper introduces an efficient marginal likelihood approach implemented in Stan for Bayesian estimation of the univariate ACE model with ordinal data, which overcomes frequentist limitations regarding small samples and zero-frequency cells while decoupling computational cost from sample size.
This paper proposes a unified statistical framework identifying two dual, maximally ignorant prior branches—heavy-tailed and compact-support "droplet"—that emerge from information-volume geometry and classify observable phenomena across a tail-shape footprint, ranging from molecular bond distributions to image representations.
This paper demonstrates that in spatially heterogeneous systems like urban migration, feature importance rankings derived from machine learning are unstable across different development stages and do not inherently reveal the direction or uniformity of a predictor's influence, necessitating separate spatial validation for policy applications.
This paper introduces the Finite-Evidence Decision Identification Procedure (FEDIP), a modular audit framework that reveals how finite validation data often fails to uniquely identify a single optimal action among competing distributional models, thereby demonstrating the necessity of explicit economic loss functions to resolve action dispersion that standard score-based selection overlooks.
This study introduces and validates a Gaussian-based refinement algorithm that successfully estimates population usual energy intake distributions from Food Balance Sheets by treating reported values as upper bounds, demonstrating significantly improved accuracy over existing FAO methods when compared to national dietary surveys across six countries.
This study empirically compares linear mixed-effects, generalized estimating equations, and two-part mixed models for estimating EQ-5D utilities in rheumatoid arthritis trials, finding that while two-part mixed models yield lower utility estimates, the choice of method has minimal impact on overall cost-effectiveness conclusions.
This paper proposes a unified methodological framework combining a Global Normative-Anchor Translated CES function with a certainty-equivalent pre-screening mechanism to resolve computational domain collapse and prevent the "Epistemic Shielding Trap" in non-compensatory composite indicators, thereby enabling accurate longitudinal data quality audits while explicitly warning against using the penalized index for cross-sectional funding allocation.