82 papers
The paper introduces BSync, a Bayesian framework that improves the synchronization of proxy paleorecords with reference chronologies by inferring a monotone time-mapping function with quantified uncertainty, outperforming existing optimization-based methods especially when independent age constraints are sparse.
This paper proposes and validates two novel g-computation algorithms that effectively address the simultaneous challenges of time-varying confounding and semi-competing events (where death precludes non-terminal outcomes) to estimate causal effects of time-varying interventions, demonstrating superior performance in simulations and revealing potential health benefits of smoking prevention in a longitudinal study of hypertension.
This paper proposes a novel trajectory-informed graph-based clustering method that integrates multi-modal longitudinal data to identify clinically relevant cancer subtypes with distinct prognostic trajectories, thereby advancing personalized oncology beyond static biomarker approaches.
This paper proposes a novel time-varying Ornstein-Uhlenbeck stochastic differential equation model that jointly analyzes individual telemetry and population-level abundance data to efficiently infer spatio-temporal dynamics, enabling improved risk assessment for wind projects and retrospective prediction of golden eagle migration origins.
This paper presents a novel framework that integrates classical structural radiomics with functional enhancement pattern mapping (EPM) features derived from liver MRI to achieve superior diagnostic classification and tumor grade stratification in liver cancer compared to traditional methods.
This paper proposes a new methodology using a Bayesian spatiotemporal model and household survey data to estimate sub-county residential displacement rates in the Central Puget Sound Region, revealing geographic variations and a temporary moderation in displacement during 2020-2021.
This paper proposes a wrapped Gaussian Markov random field (WGMRF) model to address the computational limitations of existing wrapped Gaussian process methods for large-scale, high-resolution spatial directional data, demonstrating its superior scalability and predictive performance through simulations and an application to wave direction data from the 2004 Indian Ocean Tsunami.
This scoping review systematically examines and categorizes clustering-based outcome models for clinical studies into informed and agnostic approaches, highlighting their utility in handling high-dimensional data and heterogeneous populations for applications such as risk stratification, rare disease research, and subgroup-specific treatment effect estimation.
This paper proposes a novel multivariate Hawkes process framework combining additive excitation and multiplicative inhibition to effectively model animal acoustic communication, which is validated through simulations and applied to reveal distinct interaction patterns in meerkat and baleen whale datasets.
This paper introduces and evaluates two localization strategies for a sequential Markov Chain Monte Carlo data assimilation framework, demonstrating their ability to efficiently handle high-dimensional, nonlinear, and non-Gaussian geophysical models while avoiding weight degeneracy and outperforming traditional ensemble Kalman filters in scenarios with heavy-tailed observation noise.
This longitudinal study of 238 All of Us participants utilizing four years of Fitbit data reveals that total knee and hip arthroplasty patients experience progressive preoperative activity declines followed by a staged postoperative recovery pattern, with higher immediate preoperative activity levels significantly predicting a greater likelihood of returning to habitual physical activity.
This paper proposes a two-stage adaptive design framework for cluster randomised trials that combines test approaches with Pareto optimality to enable early stopping, sample size re-estimation, and trial re-design, thereby addressing high uncertainty in correlation parameters and balancing multi-dimensional cost-efficiency objectives.
This paper establishes conservative probabilistic bounds for the spectrum of admittance matrices and linear power flow models under uncertain network parameters by leveraging random matrix concentration inequalities, thereby providing a theoretical framework to quantify approximation errors and analyze how uncertainty concentrates at critical nodes.
This paper proposes a "Test-then-Punish" framework that sustains cooperation in discounted infinitely repeated games with imperfect monitoring by embedding statistical hypothesis testing into strategic behavior, allowing players to detect deviations and enforce a Folk theorem-type result through either anytime valid sequential tests or batch-based testing.
This paper introduces a spatially explicit longitudinal sequence analysis of African political violence from 1997 to 2024, identifying six distinct conflict trajectories and their spatial interdependencies to advance conflict research beyond discrete transitions toward a holistic understanding of violence life cycles.
This paper proposes a data-driven framework using time-varying queueing models to improve long-term ICU capacity planning by capturing fluctuating admission rates and heterogeneous length-of-stay distributions, demonstrating that static heuristics like the 85% occupancy rule are inadequate for managing real-world demand variability in neonatal intensive care units.
This paper proposes a sequential "Clusters-of-Centres" algorithm that utilizes multivariate Cochran-type tests on summary statistics to identify and merge homogeneous groups in multi-centre studies, establishing asymptotic distributions and proving that a multi-round bootstrap variant can recover the true centre partition with high probability.
This paper proposes a new omnibus goodness-of-fit test for univariate continuous distributions that leverages the full covariance structure of trigonometric moments to achieve a well-calibrated asymptotic null distribution, offering a unified, plug-and-play framework with demonstrated accuracy and power across 11 common parametric families.
This tutorial presents a computationally efficient, two-step Bayesian framework for quantifying uncertainty in linear shock compression data by deriving posterior distributions for model parameters and propagating them through Rankine-Hugoniot equations to generate multiple consistent Hugoniot curves, offering a more robust and interpretable alternative to traditional least squares and bootstrapping methods.
This paper introduces behavior-decomposed linear dynamical systems (b-dLDS), a novel modeling approach that disentangles behavior-related neural dynamics from internal computations in large-scale brain recordings, demonstrating superior performance over existing supervised models and successfully scaling to tens of thousands of neurons in zebrafish hindbrain data.