194 papers
This paper establishes a general stability criterion for stochastic mirror descent algorithms to enable valid statistical inference in adaptive bandit settings, introducing regularized-EXP3 variants that simultaneously achieve minimax-optimal regret, nominal confidence interval coverage, and robustness to adversarial corruptions.
This paper proposes and validates two novel g-computation algorithms that effectively address the simultaneous challenges of time-varying confounding and semi-competing events (where death precludes non-terminal outcomes) to estimate causal effects of time-varying interventions, demonstrating superior performance in simulations and revealing potential health benefits of smoking prevention in a longitudinal study of hypertension.
This paper proposes a multivariate pseudo-empirical best linear unbiased predictor and associated bootstrap mean squared error estimators for multiple dependent small area characteristics under a multivariate nested error regression model, ensuring design consistency and flexibility for both unit-level and area-level data.
This paper proposes a novel trajectory-informed graph-based clustering method that integrates multi-modal longitudinal data to identify clinically relevant cancer subtypes with distinct prognostic trajectories, thereby advancing personalized oncology beyond static biomarker approaches.
This paper establishes the first universality-breaking frontier in shuffle privacy by characterizing the asymptotic behavior of concentrated local randomizers that fail classical Gaussian limits, proving convergence to explicit Poisson, Skellam, and compound-Poisson shift experiments and providing a complete three-regime picture of shuffle privacy limits.
This paper establishes a novel connection between robust bias correction (RBC) and bootstrap prepivoting to develop an improved nonparametric inference procedure that produces confidence intervals 17% shorter than conventional methods without compromising asymptotic coverage.
This paper proposes a computationally tractable, correctly centered internal instrument IV estimator for linear regression models with clustered data, high-dimensional controls, and complex exclusion restrictions, providing robust inference procedures illustrated through a study of spatial interference in rural Kenya.
This paper presents a preregistered simulation study comparing 28 variable selection and model averaging methods for logistic regression, finding that Bayesian model averaging with g-priors performs best in the absence of separation, while penalized likelihood approaches like LASSO are most stable when separation occurs.
This paper introduces the R package `afttest`, which provides computationally efficient martingale-residual-based diagnostic tools, including a new influence-function linear approximation method, for assessing the goodness-of-fit of semiparametric accelerated failure time models.
This paper proposes a novel surface decomposition method combined with an importance sampling strategy to efficiently analyze the sensitivity of first-passage dynamic reliability in linear systems under Gaussian random excitations, enabling the reuse of function evaluations for numerous design parameters with a computational cost of only 10² to 10³ evaluations.
This paper proposes a novel time-varying Ornstein-Uhlenbeck stochastic differential equation model that jointly analyzes individual telemetry and population-level abundance data to efficiently infer spatio-temporal dynamics, enabling improved risk assessment for wind projects and retrospective prediction of golden eagle migration origins.
This paper proposes new power formulas and a resampling-based estimation method for designing and analyzing clinical trials that compare single or multiple quantiles of right-censored survival data, offering a robust alternative to traditional methods when the proportional hazards assumption is violated.
This paper proposes a doubly robust estimator for the average treatment effect on the treated in semiparametric difference-in-differences models by incorporating covariate balancing, and derives a novel, bias-corrected model selection criterion that outperforms existing information criteria in both simulation and real-world data applications.
This paper introduces efficient algorithms for updating the R matrix in QR decomposition without recalculating Q, significantly reducing computational costs for dynamic statistical applications such as regression, filtering, and model selection while maintaining accuracy.
This paper proposes a novel Bayesian nonparametric model based on a location-scale Dirichlet process mixture of centered Erdős–Rényi kernels to identify clusters of networks with similar connectivity patterns, demonstrating its theoretical consistency, superior performance in simulations, and practical applicability to large-scale human brain network data.
This paper proposes a hierarchical Bayesian estimator for the variance of fine stratification surveys that outperforms existing nonparametric Bayes and kernel-based methods by achieving lower frequentist bias and mean squared error, as demonstrated through simulations and real-world data analysis.
This paper utilizes a Monte Carlo simulation study to demonstrate that while moderate augmentation in the Finite Selection Model improves covariate balance, excessive augmentation increases variance, thereby offering practical guidelines for selecting the optimal augmentation parameter to balance efficiency and feasibility.
This paper presents a novel framework that integrates classical structural radiomics with functional enhancement pattern mapping (EPM) features derived from liver MRI to achieve superior diagnostic classification and tumor grade stratification in liver cancer compared to traditional methods.
This paper introduces DistBART, a scalable Bayesian nonparametric method for distribution regression that models the regression function using a linear functional with a BART prior, offering both theoretical guarantees on posterior concentration and practical efficiency through random-feature approximations.
This paper proposes a new methodology using a Bayesian spatiotemporal model and household survey data to estimate sub-county residential displacement rates in the Central Puget Sound Region, revealing geographic variations and a temporary moderation in displacement during 2020-2021.