143 papers
This paper introduces DR-FoS, a novel doubly-robust estimator for the Functional Average Treatment Effect (FATE) that ensures consistent estimation and valid simultaneous inference even when either the outcome or treatment assignment model is misspecified, demonstrating its effectiveness through simulations and a real-world application to the SHARE dataset.
This paper introduces efficient, parameter-free geodesic slice sampling algorithms for generating samples from probability distributions on the sphere, proving their uniform ergodicity and demonstrating superior performance over standard methods like random-walk Metropolis-Hastings and Hamiltonian Monte Carlo in challenging directional data scenarios.
This paper establishes faster minimax convergence rates for a binary plug-in classification procedure applied to time-homogeneous SDE paths with space-dependent coefficients under low-noise conditions, by deriving a crucial exponential inequality and analyzing the lower bound on the empirical classifier's excess risk.
This paper introduces a scalable multitask Gaussian process model with a fully separable kernel structure that effectively handles functional covariates and correlated tasks, demonstrating superior accuracy and computational efficiency over single-task approaches in predicting the behavior of complex mechanical systems like riveted assemblies with limited data.
This paper formalizes the order-dependence of the moving-range sigma estimator by decomposing its total variance into order-invariant and adjacency-specific components via random permutation, revealing that under normal sampling, the adjacency effect accounts for the majority of its efficiency loss relative to the standard deviation estimator.
This paper proposes a novel class of covariate-adaptive randomization procedures that effectively balance specified covariates while ensuring the asymptotic variance of unspecified covariate imbalances does not exceed that of simple randomization, thereby avoiding inflated variances and the associated "shift problem" that invalidates standard treatment effect tests.
This paper introduces Sigmoid-FTRL, an adaptive experimental design that overcomes the non-convexity challenges of AIPW estimators to achieve the minimax optimal Neyman Regret rate of while providing valid asymptotic inference.
This paper introduces the Poisson tensor completion (PTC) estimator, which leverages inter-sample relationships and models histogram bins as a non-homogeneous Poisson process to achieve a low-rank, non-negative tensor decomposition that significantly outperforms standard histogram-based estimators for sub-Gaussian distributions.
This paper introduces an efficient algorithm that rapidly computes a complete curve of post hoc false discovery proportion bounds along a path of increasing selection sets by leveraging the forest structure of the reference family and the incremental nature of adding single hypotheses.
This paper establishes the distributional stability of sparse inverse covariance matrix estimators by deriving explicit local Lipschitz bounds on the Kantorovich distance between their distributions under true and contaminated data scenarios, while also extending these results to standard covariance and eigenvalue estimators.
This paper develops an asymptotic expansion formula for bootstrap coverage probabilities in high dimensions to theoretically explain why third-moment matching wild bootstrap methods outperform normal approximations, demonstrating that such methods achieve second-order accuracy without studentization under specific covariance structures or via a double wild bootstrap approach.
This paper demonstrates that by strategically combining sample splitting with specific nuisance function tuning strategies (such as undersmoothing or oversmoothing), both plug-in and first-order bias-corrected estimators can achieve minimax rates of convergence for doubly robust functionals across all Hölder smoothness classes, overcoming limitations of existing literature.
This paper proposes a nonparametric two-sample hypothesis test for low-rank random graphs of differing sizes, utilizing maximum mean discrepancy on optimally transported graph embeddings to determine if the networks originate from the same distribution.
This paper proposes a consistent, moment-based Bayesian testing procedure using exponentially tilted empirical likelihood to detect endogeneity in linear regression models, demonstrating its effectiveness through simulations and real-world applications involving automobile and airline markets.
This paper proposes TV-Select, a unified framework that simultaneously identifies relevant variables and distinguishes between constant and time-varying effects in longitudinal models by employing a doubly penalized B-spline approach with group Lasso and roughness penalties to achieve accurate structural recovery, smooth estimation, and improved predictive performance.
This paper proposes a nonparametric density estimation method for compositional data with missing values using an adaptive Dirichlet kernel and inverse probability weighting, demonstrating its superior finite-sample performance over log-ratio transformation approaches and its practical utility in analyzing leukocyte composition data.
This paper proposes a unified calibration framework that integrates heterogeneous internal and auxiliary information into randomized experiments under covariate-adaptive randomization via convex optimization, ensuring asymptotic validity and a no-harm efficiency guarantee while accommodating scenarios with growing numbers of strata and information sources.
This paper introduces a novel Fréchet regression framework for multivariate distributional responses that leverages the nonparanormal transport metric to efficiently decompose the problem into marginal and dependence regressions, offering theoretical guarantees on convergence and dimensionality while demonstrating practical utility in continuous glucose monitoring.
This paper establishes that for Bayesian inference of planted matchings between correlated 1D point sets under critical scaling, the posterior distribution admits a local approximation with a well-defined infinite-volume limit in the partial matching case, whereas the exact matching case requires global sorting and a flow-based indexing to define its asymptotic marginal statistics.
This paper proposes a minimax-optimal estimator for varying coefficient models using truncated Laguerre series, establishing its asymptotic normality and finite-sample performance through theoretical analysis, simulations, and real-data applications.