227 papers
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.
Motivated by robotic swarm monitoring, this paper proposes the Multi-rank Subspace-CUSUM (MRS-C) procedure for detecting low-rank changes in high-dimensional streaming data, proving its first-order asymptotic optimality and demonstrating its effectiveness through theoretical analysis and real-world applications.
This paper extends the wgsLR model for forensic shotgun DNA sequencing to accommodate asymmetric and unknown genotyping error probabilities between trace and reference samples, demonstrating the model's robustness and providing conservative strategies for calculating weight of evidence, all implemented in an updated R package.
This paper proposes a principled Bayesian response-adaptive randomization method that stabilizes the high variability of Thompson sampling by introducing a null hypothesis of equal treatment effectiveness, thereby balancing patient allocation to superior treatments with statistical reliability and inferential validity.
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 paper introduces the statistical foundations of multivariate and spatial extreme value theory and provides practical, step-by-step guidance for applying the R package ExtremalDep to assess extreme event risks in real-world scenarios.
This paper introduces the Bayesian Hierarchical Adjustable Random Partition (BHARP) model, a self-contained framework that utilizes a finite mixture model and reversible-jump Markov chain Monte Carlo sampling to automatically identify treatment effect heterogeneity and adjust information borrowing in adaptive enrichment trials, thereby outperforming existing methods in accuracy and precision.
This paper develops a non-parametric sequential framework using e-values and e-processes to perform anytime-valid comparative backtests for general elicitable risk measures, offering robust performance under dependence and misspecification while introducing a modified three-zone approach for more informative model evaluation.
This paper introduces a novel lasso-penalized threshold hidden Markov model that effectively distinguishes meaningful behavioral shifts from spurious noise, revealing that narwhals react to vessel disturbances up to 4 kilometers away by altering their movement patterns and diving deeper.
This paper challenges the strict behaviorist interpretation that a realized confidence interval either covers the parameter or it does not, arguing instead that a model-based view treating coverage as a predictive probability is mathematically consistent with long-run error rates and offers a more nuanced framework for post-data inference.
This paper proposes a decision-theoretic interpretation of confidence intervals as optimal probabilistic forecasts for coverage, demonstrating that the nominal confidence level is the unique optimal constant prediction and that design-based refinements can improve predictive performance without relying on Bayesian priors.
Although providing students with a choice of real-data contexts for homework did not significantly improve grades in an introductory statistics course, it substantially enhanced their engagement, motivation, and sense of autonomy, leading the authors to recommend incorporating varied, student-relevant contexts as a key pedagogical tool.