218 papers
This paper establishes the fundamental framework for quaternion machine learning by introducing augmented statistics, widely linear models, quaternion calculus, and mean square estimation to facilitate the adoption of this multidimensional domain.
This paper proposes a novel test-then-pool framework that leverages kernel-based distributional equivalence testing and resampling methods to safely fuse historical and concurrent control data in randomized controlled trials, thereby improving statistical power while rigorously controlling Type-I error rates.
This paper introduces Distributionally Balanced Designs (DBD), a new probability sampling method that minimizes the energy distance between sample and population auxiliary distributions through optimized circular ordering, thereby achieving superior representativeness and lower estimation variance compared to existing state-of-the-art techniques, particularly in resource-constrained fields like ecology and forestry.
This paper proposes a novel Bayesian model calibration framework that integrates a Gaussian process discrepancy directly within the simulator to attribute model-form errors to input parameter uncertainty, offering a computationally tractable alternative to the traditional Kennedy-O'Hagan method for calibrating Discrete Dislocation Dynamics models against Molecular Dynamics observations.
This paper introduces Local Adjacency Spectral Embedding (LASE), a novel method that overcomes the limitations of global spectral embedding by uncovering locally low-dimensional network structures through weighted spectral decomposition, thereby improving local reconstruction, visualization, and theoretical guarantees via finite-sample bounds and spectral gap analysis.
This paper proposes a novel approach to batch Bayesian optimal experimental design by lifting the optimization problem to the space of probability measures, characterizing the optimal design as a Gibbs distribution, and developing scalable particle-based algorithms via Wasserstein gradient flows to efficiently explore complex, non-convex utility landscapes.
This paper establishes a central limit theorem for large network formation models with strategic interactions and homophily by adapting stabilization conditions from geometric graphs and deriving interpretable primitive conditions based on branching process theory to justify practical inference procedures.
This paper proposes a novel, computationally efficient estimator for polychoric correlation that minimizes a robust loss function to withstand partial model misspecification—such as careless responding—without requiring assumptions about the nature or extent of the errors, thereby offering a more reliable alternative to traditional maximum likelihood estimation in rating data analysis.
This paper proposes a Bayesian framework for synthesizing incomplete SARS-CoV-2 data to estimate total infections and transmission dynamics, demonstrating that Hamiltonian Monte Carlo offers robust inference, mobility data enhances prediction, and informative priors combined with phase plane analysis provide superior decision support compared to restrictive assumptions.
This paper establishes the asymptotic convergence and iteration complexity of block majorization-minimization algorithms for smooth nonconvex optimization problems with block constraints on Riemannian manifolds, demonstrating their broad applicability and superior performance over standard Euclidean approaches.
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 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 proposes an innovative sampling approach using temporally shifted non-events to enable efficient estimation of global covariate effects within relational event models, demonstrating its effectiveness through simulations and a case study on Washington D.C. bike-sharing data that reveals significant impacts of weather and time of day on ride dynamics.
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 method for identifying causal generalized linear models by leveraging Pearson risk invariance and maximum expected likelihood, enabling causal discovery from a single data environment without requiring multiple heterogeneous settings.
This paper proposes a new Metropolis-Hastings algorithm that leverages control variates and subsampling to achieve exact, efficient Bayesian inference on large datasets while satisfying detailed balance and outperforming existing methods in computational efficiency and required subsample size.
This paper introduces an exploratory restricted latent class model that accommodates polytomous responses, ordinal multi-attribute states with correlated attributes via a multivariate probit specification, and respondent-level covariates, demonstrating its effectiveness in recovering parameters and revealing complex latent structures in depression diagnosis beyond traditional single-factor approaches.
This paper introduces a novel nonparametric framework for detecting changepoints in the mean direction of toroidal and spherical meteorological data by leveraging intrinsic geometry to define a curved dispersion matrix and Mahalanobis distance, establishing the statistical properties of the proposed tests and validating them on wind-wave directions and cyclonic storm paths.
This paper proposes a stable survival extrapolation framework that integrates Bayesian mortality models with flexible parametric polyhazard models to leverage external registry data as an anchor, thereby improving the robustness and interpretability of mean survival estimates in complex clinical scenarios such as breast cancer, melanoma, and cardiac arrhythmia.
This paper demonstrates that a noncentral Wishart mixture of noncentral Wishart distributions with identical degrees of freedom remains a noncentral Wishart distribution, a result used to derive the finite-sample distribution for testing random effects in two-factor and general factorial design models with multivariate normal data.