190 papers
This paper proposes a simple, conservative variance estimator for sums of random vectors with heterogeneous means under two-way cluster or weak dependence, addressing the underestimation and oversized tests caused by standard estimators while establishing its asymptotic validity.
This paper introduces CausalTimePrior, a novel framework for generating synthetic temporal structural causal models with paired observational and interventional data to enable the training of Prior-data fitted networks (PFNs) for in-context causal effect estimation in time series.
This paper introduces a partition-based functional ridge regression framework that decomposes coefficient functions into dominant and weaker components to apply differential penalization, thereby improving numerical stability, interpretability, and predictive performance in high-dimensional functional linear models without relying on explicit variable selection.
This paper argues that standard stateful pseudorandom number generators undermine the causal validity of agent-based models by linking random draws to execution order rather than specific events, and proposes using event-keyed hashing with counter-based generators to restore causally coherent paired counterfactual comparisons.
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.
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 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.
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 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 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 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 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 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 argues that while many common causal estimands in network settings fail to simultaneously offer interpretable summaries of unit-level effects and support optimal policy choice due to the mismatch between homogeneous exposure assumptions and heterogeneous realities, the expected average outcome serves as a superior estimand that fulfills both criteria.
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 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 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 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 proposes a nonlinear local mean-field approximation method that utilizes a first-order Taylor expansion of hazard rates to enable efficient and robust parameter estimation for quasi-reaction systems, particularly outperforming existing SDE and ODE-based approaches when dealing with large time gaps between observations and stiff biological dynamics.
This paper proposes a multichannel profile covariance (MPC) control chart based on functional graphical models and a nonparametric combination of likelihood-ratio tests to effectively detect subtle, sparse changes in covariance structures that traditional mean-focused methods often miss.