182 papers
This paper extends the definition of the multidimensional Dickman distribution to vector-valued random elements characterized as fixed points of a specific affine transformation, proving their infinite divisibility and operator selfdecomposability while identifying cases where they emerge as limiting distributions.
This paper introduces a general framework for "central subspace data depths," a new class of statistical tools that order multivariate data from a central subspace rather than a single point, thereby extending symmetry-based analysis to higher-dimensional structures and demonstrating practical utility in fraud detection.
This paper proposes a novel semi-parametric approach for conditional copula models using Bayesian Additive Regression Trees (BART) enhanced by a loss-based prior to mitigate overfitting and an adaptive Reversible Jump Markov Chain Monte Carlo algorithm to efficiently model complex, non-smooth dependencies, demonstrating its effectiveness through both theoretical recovery of true structures and a case study on the impact of GDP on life expectancy and literacy rate correlations.
This paper presents a vector generalization of the curvature-corrected Cramér-Rao bound for multivariate parameters in the nonasymptotic regime, utilizing extrinsic geometry and sum-of-squares relaxations to derive directional and matrix-valued refinements that offer more faithful estimation limits than classical second-order corrections, as demonstrated through curved Gaussian and spherical multinomial models.
This paper presents a geometric refinement of the classical Cramér-Rao bound in the non-asymptotic regime by leveraging the extrinsic curvature of the statistical model manifold's square root embedding to derive tighter, curvature-aware lower bounds on estimator variance.
This paper proposes a conceptually intuitive, easy-to-implement, and dimension-agnostic statistical testing method based on rejection sampling that demonstrates power comparable to uniformly most powerful (unbiased) tests across various empirical scenarios, including mean comparisons and goodness-of-fit assessments.
This paper introduces DESA, an R package available on CRAN that models, detects, and evaluates alerts for influenza epidemics using elementary school absenteeism data, while also providing simulation tools for community-level research.
This paper characterizes the Palm distributions of superposed independent point processes through a mixture representation, enabling new statistical inference strategies for minimum contrast estimation of corrupted processes and likelihood-based analysis of shot noise Cox processes.
This paper proposes a one-shot, privacy-preserving federated framework for time-to-event analysis that utilizes pseudo-observations and a covariate-wise debiasing procedure to achieve flexible, accurate modeling of both proportional and non-proportional hazards without requiring iterative communication or pooling individual-level data.
This paper introduces parallel algorithms for simulating zeroth-order Metropolis Markov chains based on Picard maps that significantly accelerate convergence in high-dimensional settings by leveraging parallel computing to generate samples from log-concave distributions using only point-wise evaluations of the log-density.
This paper introduces a restricted latent class hidden Markov model for longitudinal polytomous data that incorporates respondent-specific covariates, establishes its identifiability, validates its performance through simulations, and demonstrates its practical utility in analyzing mathematics examination and emotional state data.
This paper proposes an extended Nested Error Regression Model with High-Dimensional Parameters (NERHDP) featuring an efficient estimation algorithm and novel out-of-sample prediction methods to provide robust, scalable, and accurate empirical best predictors for small area poverty indicators, as demonstrated through an application to Albania's municipal data.
This paper proposes a novel approach using persistent homology on a filtered witness complex to identify geographic gaps in cooling center coverage across four US cities, demonstrating that combining these topological findings with traditional heat vulnerability indices provides a more holistic assessment of heat-related mortality risk.
This paper develops a theoretical framework and novel nonparametric identification formulas to address the challenges of forecasting causal effects of future interventions by clarifying the necessary structural assumptions and estimands for transporting causal knowledge across time, particularly in the presence of time-varying confounders and effect modifiers.
This paper introduces a model for network time series using Euclidean mirrors to represent latent position processes, demonstrating that spectral estimation can effectively localize first-order changepoints where the rate of network evolution shifts, a method validated through both simulations and real-world organoid network data.
This paper introduces DELVE, a spectral graph filtering method that extracts modality-specific latent variables by constructing graphs for each modality and designing a filter to attenuate shared signals while preserving unique components, with theoretical convergence guarantees and empirical validation on synthetic and real datasets.
This paper introduces PRS-Bridge, a robust Bayesian method for constructing polygenic risk scores that resolves posterior impropriety through projected summary statistics and employs a flexible bridge prior to achieve superior performance across diverse scenarios.
This paper proposes a simple, intuitive approach to achieve accurate uncertainty quantification for Bayesian inference in misspecified or approximate models by substituting the standard posterior with an alternative that conveys the same information, thereby avoiding the need for explicit Gaussian approximations or post-processing.
This paper presents an efficient method for sketching stochastic valuation functions by constructing discretized item value distributions with logarithmic support size that provide constant-factor approximations for monotone subadditive or submodular functions, thereby enabling scalable solutions for complex optimization problems like welfare maximization.
This paper reports the discovery of "murmurations," a new arithmetic phenomenon identified through AI-assisted analysis of large datasets that encodes subtle information about Frobenius traces and connects to the Birch and Swinnerton-Dyer conjecture and random matrix theory.