82 papers
This study analyzes NBA play-by-play and Last Two Minutes data to find that while referees exhibit a home-court advantage bias (which has decreased since the pandemic) and show statistical favoritism toward specific players, there is no evidence of negative bias against particular players, teams, or racial groups.
This paper introduces DESA, an R package available on CRAN that models, detects, and evaluates alerts for influenza epidemics using elementary school absenteeism data, while also providing simulation tools for community-level research.
This paper proposes a one-shot, privacy-preserving federated framework for time-to-event analysis that utilizes pseudo-observations and a covariate-wise debiasing procedure to achieve flexible, accurate modeling of both proportional and non-proportional hazards without requiring iterative communication or pooling individual-level data.
This paper proposes an extended Nested Error Regression Model with High-Dimensional Parameters (NERHDP) featuring an efficient estimation algorithm and novel out-of-sample prediction methods to provide robust, scalable, and accurate empirical best predictors for small area poverty indicators, as demonstrated through an application to Albania's municipal data.
This paper proposes a novel approach using persistent homology on a filtered witness complex to identify geographic gaps in cooling center coverage across four US cities, demonstrating that combining these topological findings with traditional heat vulnerability indices provides a more holistic assessment of heat-related mortality risk.
This paper introduces a Hierarchical Decomposition prior framework for Bayesian species distribution models that reparametrizes variance components to enable transparent, ecologically meaningful control over the relative contributions of environmental, spatial, and temporal processes, as demonstrated through improved interpretability and comparable predictive performance on fish distribution data.
This paper proposes a unified anomaly detection framework that identifies outliers as observations with unusually low model probability by converting them into univariate surprisal scores and estimating their tail probabilities using either empirical or extreme-value methods, demonstrating robustness against model misspecification in complex datasets.
This paper introduces MM-test, a distribution-free method that combines a novel quasi-likelihood ratio statistic with a knockoff procedure to accurately identify spatially variable genes and control false discovery rates in both 2D and 3D spatial transcriptomics data, outperforming existing methods in benchmarking and real-world applications.
AgroDesign is a Python framework that bridges the gap between agricultural experimental design and statistical inference by automatically translating structured designs into valid linear models, thereby minimizing subjective analyst choices and enhancing the reproducibility and accuracy of agricultural data analysis.
This paper introduces a BMW-GAM copula workflow that utilizes Bayesian generalized additive models in moving windows to provide interpretable uncertainty quantification for critical energy systems facing compound extreme weather events, as demonstrated through an analysis of temperature, wind speed, and solar irradiance data.
This study demonstrates that the longitudinal progression of radiologic pleuroparenchymal fibroelastosis (PPFE), quantified via automated analysis of low-dose CT scans, independently predicts increased mortality and adverse respiratory outcomes in large lung cancer screening cohorts.
This paper proposes an unsupervised prognostics framework that utilizes unlabeled run-to-failure data to simultaneously identify latent failure modes and select informative sensors, thereby enabling accurate remaining useful life prediction for autonomous deep-space habitats under multiple unknown failure conditions.
CERES is a novel, open-access, and continuously running probabilistic forecasting system that fuses six global data streams to generate weekly, machine-readable 90-day ahead risk estimates for acute food insecurity across 43 high-risk countries, with a unique commitment to public prospective verification of all predictions.
This paper argues that citation visibility in generative search should be treated as a stochastic distribution requiring uncertainty estimates rather than a fixed value, demonstrating through empirical analysis of multiple AI platforms that single-run measurements are misleadingly precise and that robust statistical sampling is essential for accurate domain performance assessment.
This paper establishes the distributional stability of sparse inverse covariance matrix estimators by deriving explicit local Lipschitz bounds on the Kantorovich distance between their distributions under true and contaminated data scenarios, while also extending these results to standard covariance and eigenvalue estimators.
This paper proposes TV-Select, a unified framework that simultaneously identifies relevant variables and distinguishes between constant and time-varying effects in longitudinal models by employing a doubly penalized B-spline approach with group Lasso and roughness penalties to achieve accurate structural recovery, smooth estimation, and improved predictive performance.
This paper proposes a transparent, model-based approach to isolate and decompose the energy consumption effects of intelligent district heating control algorithms from other building changes, addressing the limitations of existing evaluation methods using a decade of real-world data.
This paper proposes a nonparametric density estimation method for compositional data with missing values using an adaptive Dirichlet kernel and inverse probability weighting, demonstrating its superior finite-sample performance over log-ratio transformation approaches and its practical utility in analyzing leukocyte composition data.
This study employs Bayesian structural time-series causal inference on multi-decadal satellite data to demonstrate that the rapid expansion of nickel processing facilities at Indonesia's Morowali Industrial Park has caused a significant, quantifiable degradation of coastal water clarity, posing potential risks to the region's high-biodiversity marine ecosystems.
Using Bayesian inference and generalised extreme value regression on five CMIP6 models, this study projects that extreme temperatures in Earth's desert regions will significantly increase over the next century, with the most pronounced rises in annual maximums under severe forcing scenarios.