35 papers
This paper introduces a set of Python tools designed to bridge the gap between the theoretical framework of Kendall's 3D shape space and practical computational workflows, addressing specific limitations in existing libraries like Geomstats to facilitate advanced 3D shape analysis.
This paper introduces Gimbal Regression, a deterministic and geometry-aware local regression framework that ensures stable, auditable estimation of spatial heterogeneity by constructing directional weights from neighborhood geometry to mitigate numerical instability caused by anisotropic or low-dimensional data structures.
This paper introduces the R package `afttest`, which provides computationally efficient martingale-residual-based diagnostic tools, including a new influence-function linear approximation method, for assessing the goodness-of-fit of semiparametric accelerated failure time models.
This paper proposes a hierarchical Bayesian estimator for the variance of fine stratification surveys that outperforms existing nonparametric Bayes and kernel-based methods by achieving lower frequentist bias and mean squared error, as demonstrated through simulations and real-world data analysis.
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.
The paper introduces BLAST, a Bayesian transfer learning framework for high-dimensional linear regression that utilizes adaptive shrinkage and source selection to improve predictive accuracy, uncertainty quantification, and inference by effectively balancing information sharing across multiple data sources while mitigating negative transfer.
This study develops a network-based model to overcome the limitations of traditional homogeneous mixing assumptions in studying Frogeye Leaf Spot, revealing that early targeted roguing is the most effective management strategy while tillage practices showed no significant impact on disease spread.
This paper introduces a computationally efficient multi-level Gaussian process regression framework with exact analytic expressions for log-likelihood and posterior distributions under regular or partially regular sampling designs, enabling the analysis of large functional datasets that are otherwise intractable with standard implementations.
This paper introduces a unifying framework based on bouncy Hamiltonian dynamics that rigorously connects Hamiltonian Monte Carlo and piecewise deterministic Markov process samplers, enabling the construction of rejection-free, competitive samplers that bridge the gap between these two major Bayesian inference paradigms.
This paper introduces a bias- and variance-aware probabilistic framework for rounding error analysis that explicitly calibrates confidence parameters and accommodates biased error models, demonstrating through theoretical derivation and CUDA experiments that such an approach yields sharper, more accurate error bounds than classical methods, particularly in low-precision arithmetic.
This study introduces DisSim-FinBERT, a novel framework that combines discourse simplification with aspect-based sentiment analysis to improve the extraction of core messages and sentiment accuracy from complex financial documents like FOMC minutes.
This paper proposes a new least trimmed squares regression method that simultaneously handles missing values, resists both casewise and cellwise outliers, accommodates skewed distributions, and enables robust out-of-sample predictions.
This paper introduces Latent-IMH, an efficient Bayesian sampling method for inverse problems with expensive operators that leverages cost-effective approximations to shift computational costs to an offline phase, achieving significantly faster performance than state-of-the-art methods like NUTS.
This paper proposes a Bayesian nonparametric framework for finite mixture models with nonparametric components, establishing theoretical guarantees for component identifiability and near-polynomial posterior contraction rates while providing an efficient MCMC algorithm for inference.
This paper presents a Monte Carlo-based computational framework to analyze the steady-state distributions and stability of random differential equations with uncertain, multi-modal parameters, demonstrating its efficacy through a Rosenzweig-MacArthur predator-prey model that reveals complex, multi-modal equilibrium behaviors.