219 papers
This paper unifies the analysis of Balanced Repeated Replication and Paired Jackknife variance estimators by demonstrating that their components are independent stratum contrasts, thereby enabling the derivation of a practical formula for effective degrees of freedom via the Welch-Satterthwaite approximation.
This paper proposes a novel penalized cross-validation approach for model selection in bipartite stochastic block models that addresses the unique challenge of asymmetric overfitting and underfitting, providing the first consistency guarantee for bipartite network model selection while outperforming traditional methods.
This paper introduces a semiparametric nonlinear mixed-effects model that utilizes penalized splines for population trajectories and automatic differentiation via Template Model Builder for efficient likelihood maximization, demonstrating improved inferential performance and reduced computational burden in both simulations and an infant growth case study.
This paper proposes a dynamic Bayesian borrowing approach with simultaneous credible intervals to effectively incorporate historical control data into pre-clinical multi-arm studies with binary outcomes, demonstrating that this method can significantly reduce animal use in toxicology while maintaining control over the familywise error rate and protecting against data drift.
This paper introduces CIM, a two-stage framework that compresses high-dimensional network dynamics into low-dimensional exposure mappings to learn shape-constrained response functions and optimize seed set selection for maximizing steady-state causal outcomes with provable theoretical guarantees.
This paper introduces a robust sequential hypothesis testing framework based on generalized estimating equations that accommodates a wider range of hypotheses, provides novel asymptotic theory for joint covariance matrices, and supports multiple imputation for incomplete longitudinal data, as demonstrated through simulations and a real-world hepatitis C study.
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 addresses the challenges of hyperparameter selection and computational scalability in multi-treatment Individual Treatment Effect estimation by deriving a novel generalization bound, proposing an optimal theoretical estimator for balancing weights, and introducing a scalable Treatment Aggregation strategy within a generative Multi-Treatment CausalEGM framework that outperforms traditional models in accuracy and efficiency.
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 discrepancy directly into the simulator via Gaussian processes, challenging the traditional Kennedy and O'Hagan approach of treating discrepancy as a separate term, and demonstrates its effectiveness in calibrating Discrete Dislocation Dynamics models against Molecular Dynamics observations of critical stress.
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 and analyzes a convex estimator for the drift matrix of high-dimensional Lévy-driven Ornstein–Uhlenbeck processes, demonstrating that exploiting a low-rank plus sparse structure yields improved dimension dependence in the non-asymptotic risk bound compared to purely sparse approaches while maintaining consistent behavior across different Lévy regimes.
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 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 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.