240 papers
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.
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.
This paper addresses the underexplored challenge of variable selection for linear regression imputation in survey data by defining an optimal model via an oracle loss function, analyzing the consequences of model misspecification, and proposing a methodological framework for constructing asymptotically valid and optimal confidence intervals.
This paper proposes a new least trimmed squares regression method that simultaneously handles missing values, resists both casewise and cellwise outliers, accommodates skewed distributions, and enables robust out-of-sample predictions.
This paper presents a scalable multi-fidelity tensor emulator that integrates low- and high-resolution Arctic sea ice simulation data using tensor decomposition and Gaussian processes to efficiently generate accurate predictions with well-calibrated uncertainty, significantly outperforming single-fidelity approaches in reducing both computational cost and prediction error.
This paper proposes a unified pipeline utilizing ANOVA Simultaneous Component Analysis (ASCA) to model and interpret cyclostationary time series by effectively handling multivariate data, multiple time scales, and autocorrelation, as demonstrated through case studies on mountain lake water temperatures and airborne pollen trends.
This paper proposes a series-based analytic continuation strategy using Padé approximation on the cumulant expansion of the logarithmic Laplace transform to accurately pre-distort input sequences, thereby revitalizing autoregressive processes for the efficient and precise simulation of non-Gaussian radar clutter.
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.