182 papers
This paper establishes uniform Lorden-type moment bounds for the overshoot of random walks with sign-changing increments from standard exponential families in the small-drift regime, demonstrating that these bounds improve to a constant of 1 for large barriers and providing explicit exponential convergence rates interpreted through optimal transport metrics.
While acknowledging the ethical motivation behind a proposed "Hippocratic" utility function that prioritizes saving lives by avoiding harmful treatments over saving them through intervention, the author argues that the practical scope of this decision criterion is limited, as demonstrated by a counterexample.
This paper proposes a Bayesian adaptive enrichment design that leverages aggregate historical data via a normalized power prior to inform individualized treatment recommendations, demonstrating through simulations and a motivating obstructive sleep apnea trial that this approach improves statistical power and efficiency compared to non-borrowing designs.
This paper determines the exact attainable region between Chatterjee's rank correlation and Blest's rank correlation over all bivariate copulas by solving a constrained optimization problem via Karush–Kuhn–Tucker conditions, which yields a novel extremal copula family providing closed-form expressions that explicitly parametrize the boundary of the region.
This paper establishes the limit distributions for the last occurrence and total count of times a strongly consistent estimator deviates from its target by at least as , providing a unified framework applicable to parametric and nonparametric settings that yields new optimality results for maximum likelihood estimators and methods for constructing sequential confidence sets.
This paper investigates the statistical limits of detecting multiple inhomogeneous submatrices within a large Gaussian random matrix under both arbitrary and consecutive index placement regimes, establishing information-theoretic lower bounds and proposing matching algorithms for mean-shift and variance-shift signal models.
This paper introduces a Hierarchical Decomposition prior framework for Bayesian species distribution models that reparametrizes variance components to enable transparent, ecologically meaningful control over the relative contributions of environmental, spatial, and temporal processes, as demonstrated through improved interpretability and comparable predictive performance on fish distribution data.
This paper proposes a unified anomaly detection framework that identifies outliers as observations with unusually low model probability by converting them into univariate surprisal scores and estimating their tail probabilities using either empirical or extreme-value methods, demonstrating robustness against model misspecification in complex datasets.
This paper investigates second-order asymptotics for the number of times a strongly consistent estimator deviates from its target by more than , introducing a concept of "asymptotic relative deficiency" to distinguish between estimators with identical first-order efficiency and demonstrating that specific finite-sample corrections (such as using for normal variance) minimize the expected number of such errors.
This paper proposes a fully Bayesian framework for unnormalized models that leverages noise contrastive estimation and Pólya-Gamma data augmentation to bypass the intractable normalizing constant, enabling principled uncertainty quantification without the tuning required by score-based alternatives.
This paper introduces a novel sampling framework using multivariate temporal point processes modeled as coupled infinite-server queues to efficiently sample from discrete distributions with downward-closed support, demonstrating superior performance over existing birth-death and Zanella processes while enabling biologically plausible recurrent neural network applications.
This paper introduces MM-test, a distribution-free method that combines a novel quasi-likelihood ratio statistic with a knockoff procedure to accurately identify spatially variable genes and control false discovery rates in both 2D and 3D spatial transcriptomics data, outperforming existing methods in benchmarking and real-world applications.
AgroDesign is a Python framework that bridges the gap between agricultural experimental design and statistical inference by automatically translating structured designs into valid linear models, thereby minimizing subjective analyst choices and enhancing the reproducibility and accuracy of agricultural data analysis.
This paper introduces a BMW-GAM copula workflow that utilizes Bayesian generalized additive models in moving windows to provide interpretable uncertainty quantification for critical energy systems facing compound extreme weather events, as demonstrated through an analysis of temperature, wind speed, and solar irradiance data.
This paper proposes a framework and develops several estimators with varying degrees of robustness for estimating and conducting inference on heterogeneous principal causal effects under principal ignorability, demonstrating their theoretical properties and practical application through the Camden Coalition hotspotting randomized trial.
This paper introduces a convex optimization framework for univariate nonparametric shape-constrained density estimation using -Wasserstein projection, specifically analyzing non-increasing and log-concave densities to establish their structural properties, propose a discretized implementation, and compare their performance against maximum likelihood estimators.
This paper establishes a geometric framework demonstrating that the leading-order bias of posterior functionals in variational inference is determined by the component of the functional orthogonal to the variational tangent space, thereby explaining why structured mean-field approximations systematically distort cross-block dependencies due to omitted interaction directions.
This paper proposes an efficient semiparametric estimation method using genetic instrumental variables to address sampling uncertainty in the marginal treatment effects framework, revealing that individuals most prone to excessive alcohol consumption suffer the largest adverse effects on blood pressure.
This paper introduces SEED-SET, a Bayesian experimental design framework that combines hierarchical Gaussian Processes with stakeholder-specific subjective and objective evaluations to efficiently and effectively benchmark the ethical alignment of autonomous systems in high-stakes domains.
The paper proposes HaKAN, a lightweight and interpretable time series forecasting model based on Kolmogorov-Arnold Networks with Hahn polynomial activations that effectively captures global and local temporal patterns while outperforming recent state-of-the-art methods.