219 papers
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 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.
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 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 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 proposes a Neyman-orthogonal estimator for linear multidimensional panel data with interactive fixed-effects that combines factor model methods with a weighted-within transformation to achieve parametric consistency and asymptotic normality, demonstrated through an application to beer demand elasticity.
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 proposes two-stage sequential estimators for relative risk, odds ratio, and their logarithms that guarantee a target mean-square error for any population parameters while maintaining a controlled sample size ratio and achieving high efficiency near the Cramér-Rao bound.
This paper introduces CausalPitfalls, a comprehensive benchmark designed to rigorously evaluate and expose the significant limitations of large language models in handling statistical causal inference pitfalls, such as Simpson's paradox, through both direct and code-assisted prompting protocols.
This paper introduces a fully automatic, fully Bayesian method for constructing irregular histograms that simultaneously selects the optimal number and location of bins, demonstrating consistency and minimax convergence rates while performing competitively with existing procedures in simulations.
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 introduces B-ODIL, a Bayesian extension of the Optimization of a Discrete Loss (ODIL) method that integrates PDE-based prior knowledge with data likelihood to solve inverse problems with quantified uncertainties, demonstrating its effectiveness through synthetic benchmarks and a clinical application for estimating brain tumor concentration from MRI scans.
This paper introduces a principled framework for testing excessive influence in linear least-squares models by deriving exact influence formulas and their corresponding extreme value distributions, thereby enabling rigorous hypothesis tests to distinguish between natural sampling variation and problematic data subsets across various scientific domains.
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 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.
The paper introduces cc-Shapley, a causal-context modification of Shapley values that leverages knowledge of the data-generating process to eliminate misleading feature attributions caused by collider bias and suppression, thereby providing more accurate multivariate feature importance for explainable AI and scientific discovery.
This paper introduces novel proximal causal inference methods that leverage external control data and negative control variables to reliably estimate counterfactual placebo outcomes and demonstrate the superior efficacy of cabotegravir in active-controlled HIV prevention trials, effectively addressing challenges posed by unmeasured risk differences and low event rates.