181 papers
This paper develops a causal survival framework for platform trials that demonstrates while pooling non-concurrent controls can improve precision under strict assumptions, the most robust approach to avoid bias and maintain efficiency is to target concurrent causal estimands using covariate-adjusted doubly robust estimators that rely solely on concurrent controls.
This paper reinterprets partial copulas as a nonlinear analogue of partial correlation, establishes theoretical bounds linking conditional dependence properties to the partial copula's form, and demonstrates through simulations their utility in describing covariate-adjusted dependence for causal inference applications.
This paper proposes a fully nonparametric method for constructing tolerance intervals using a calibrated Gibbs posterior based on asymmetric Laplace loss, which ensures reliable frequentist coverage and often yields shorter intervals than traditional benchmarks across diverse applications.
This paper proposes a heterogeneity-adaptive meta-analysis framework for linear models that utilizes a Kullback-Leibler divergence penalty to shrink dataset-specific distributions toward a centroid, thereby achieving superior estimation efficiency and valid inference without assuming parameter homogeneity across studies.
This paper proposes novel kernel-based equivalence tests using kernel Stein discrepancy and Maximum Mean Discrepancy to rigorously assess the absence of statistically meaningful differences between distributions, addressing the limitations of traditional goodness-of-fit tests and existing parametric methods through asymptotic and bootstrap critical value approximations.
This paper details and extends a refined mortality estimation method for evaluating cancer screening programs by incorporating maximum likelihood estimation and Poisson regression offsets to utilize all available data, thereby significantly improving statistical precision and producing narrower confidence intervals compared to traditional approaches.
Motivated by a gastric cancer trial, this paper proposes a Bayesian framework for precision clinical trials that utilizes individually weighted external data to partially borrow information for estimating subgroup effects, supported by a simulation study comparing its performance to dynamic borrowing and demonstrating its application in determining sample sizes and decision boundaries.
This paper introduces PCID, a novel permutation-based method for offline multiple change-point detection in piecewise-constant circular signals that utilizes an isolation strategy and a von Mises-derived contrast function to achieve robust performance across various noise distributions and real-world applications.
This methodological review of 100 diagnostic test accuracy meta-analyses published in 2024 reveals that while investigations of heterogeneity are common and more frequent with larger numbers of primary studies, they often suffer from limited data support for subgroups, unclear reporting of statistical models, and insufficient prespecification in protocols.
This paper proposes a model-agnostic, distribution-free approach using split and sequential conformal prediction to construct prediction intervals for high-dimensional functional time series, demonstrating their effectiveness in forecasting subnational mortality rates for Japan and Canada compared to traditional methods.
This paper establishes a strong Gaussian approximation for high-dimensional non-degenerate U-statistics with diverging dimensions under mild assumptions, utilizing a sharp martingale maximal inequality to provide a unified framework for functional limits and inference without relying on -type bounds or bootstrap arguments.
This paper introduces PATRO, a transparent and computationally efficient plug-in method that applies distinct data-independent adjustments to experimental estimates for rollout and downstream optimization decisions, thereby minimizing avoidable losses from asymmetric estimation errors and achieving performance comparable to the complex Bayes-optimal benchmark.
This paper proposes a surrogate-assisted targeted minimum loss estimator that achieves asymptotic linearity and double robustness for causal inference with delayed outcomes under administrative censoring by leveraging a surrogate-bridge representation to avoid unstable inverse-probability weighting and eliminate second-order bias without directly estimating the conditional surrogate law.
This paper proposes a novel estimation approach for updating risk prediction models that leverages transportable risk rankings from external prognostic models rather than their specific scores, thereby improving predictive performance in internal studies with novel biomarkers even when underlying risk scores differ significantly.
This paper demonstrates that a class of optimized e-value combinations remains valid even when tuning parameters are data-dependent, extending this result to a new category of "simultaneous e-variables" and proposing an improved combination test based on elementary symmetric polynomials.
This paper provides an analytical characterization of the Synthetic Control Method's degrees of freedom to derive estimable information criteria that improve model selection over cross-validation, particularly in settings with noisy donors and numerous candidates such as the Tianjin car license rationing case.
This paper demonstrates that when the prior given hyperparameters in a Bayesian hierarchical model is a canonical maximum entropy distribution, the resulting dependent marginal prior also possesses a maximum entropy property, but with respect to a constraint on the marginal distribution of a function of the unknown parameters, thereby clarifying the implicit information assumptions of such models.
This paper introduces Gimbal Regression, a deterministic and geometry-aware local regression framework that ensures stable, auditable estimation of spatial heterogeneity by constructing directional weights from neighborhood geometry to mitigate numerical instability caused by anisotropic or low-dimensional data structures.
This paper proposes a novel operator-level ARCH framework for data in general separable Hilbert spaces that models the full evolution of the conditional covariance operator, establishing conditions for stationarity and deriving consistent estimators, which are validated through simulations and an application to high-frequency intraday returns.
This paper establishes a general theoretical framework for Outcome Weighted Learning that extends convergence guarantees to Matern and Gaussian kernels under various conditions, while proposing efficient algorithms that demonstrate strong performance in both simulations and a real-world clinical application.