252 papers
This paper demonstrates how the "garden of forking paths" creates invisible uncertainty in secondary analyses of international large-scale assessments like TALIS 2018 by conducting a multiverse analysis that reveals how nearly half a million plausible analytic pathways can yield widely varying results, thereby calling for more robust analytical practices.
This paper introduces a family of parsimonious mixture models for matrix variate skewed distributions that utilize variance-mean mixtures of normal distributions with parameter constraints to reduce complexity and enable effective clustering of high-dimensional data using an Expectation-Conditional Maximization algorithm.
This paper introduces a fast and stable product-limit style EM algorithm combined with a modified iterative convex minorant step to efficiently compute the non-parametric maximum likelihood estimator for survival functions using left-truncated and interval-censored data, demonstrating superior convergence and scalability over existing methods.
This paper proposes a robust permutation test for a modified Spearman rank correlation coefficient that utilizes univariate martingale residuals to effectively handle right-censored data, ensuring asymptotically correct Type I error control and reducing to the classical test when censoring is absent.
This paper demonstrates that scalar baseline-to-endpoint contrasts in chronic longitudinal trials often fail to uniquely capture multidomain stabilization due to non-injective mappings, arguing for the adoption of trajectory-based estimands to better align with complex clinical objectives.
This paper introduces DriftGuard, a two-stage auditing framework that establishes theoretical detectability boundaries for distribution shift and provides a rigorous, label-budgeted protocol to diagnose coverage failure and recalibrate conformal prediction models under both covariate and concept shifts.
This study demonstrates that exposure-linked differential recording of continuous outcomes, such as visually estimated blood loss, can fabricate large, robust-looking treatment effects in real-world evidence through systematic measurement error rather than true clinical differences, necessitating specific diagnostic checks and reliance on objective measures to avoid spurious conclusions.
This paper presents an uncertainty-aware, spatially informed mixture model with shrinkage and class-weighting mechanisms that significantly improves the classification of imbalanced CO₂ plume states in the Sleipner benchmark by identifying interpretable depth-organized regimes and providing posterior uncertainty estimates.
This paper proposes Bayesian kernel machine meta-regression (BKMMR), a flexible second-stage modeling framework that overcomes the limitations of conventional linear meta-regression by automatically capturing nonlinearities and interactions in multi-location environmental epidemiology data, thereby improving estimation accuracy, prediction, and downscaling capabilities as demonstrated through simulations and an air pollution study in South Korea.
This study demonstrates that the Right-Truncated Peaks-Over-Threshold (RT-POT) model outperforms traditional Generalized Extreme Value and Generalized Pareto Distribution approaches by providing more precise parameter estimates and realistic upper endpoints for managing extreme water levels at Ghana's Akosombo Dam under natural truncation.