177 papers
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 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 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 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 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 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 develops self-normalization-based structural break tests for predictive quantile and CoVaR regressions that remain valid regardless of predictor stationarity, thereby enabling robust detection of changes in forecasting power for risk and systemic risk models.
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 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.
This paper proposes a robust identification approach to derive sharp bounds on the causal effects of school assignment despite strategic preference misreporting, demonstrating significant heterogeneity in graduation outcomes using data from Chile's university matching system.
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.