Randomized incomplete \(U\)-statistics in high dimensions
From MaRDI portal
Publication:2284368
DOI10.1214/18-AOS1773zbMath1435.62075arXiv1712.00771MaRDI QIDQ2284368
Publication date: 15 January 2020
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1712.00771
bootstrapGaussian approximationdivide and conquerincomplete \(U\)-statisticsBernoulli samplingsampling with replacementrandomized inference
Hypothesis testing in multivariate analysis (62H15) Central limit and other weak theorems (60F05) Bootstrap, jackknife and other resampling methods (62F40) Approximations to statistical distributions (nonasymptotic) (62E17)
Related Items (16)
Edgeworth expansions for network moments ⋮ Rates of convergence for random forests via generalized U-statistics ⋮ A robust bootstrap change point test for high-dimensional location parameter ⋮ High-dimensional central limit theorems for homogeneous sums ⋮ Distributed inference for two‐sample U‐statistics in massive data analysis ⋮ Inference for High-Dimensional Exchangeable Arrays ⋮ Testing the martingale difference hypothesis in high dimension ⋮ Central limit theorems for high dimensional dependent data ⋮ DESIGN BASED INCOMPLETE U-STATISTICS ⋮ High-dimensional central limit theorems by Stein's method ⋮ Approximating high-dimensional infinite-order \(U\)-statistics: statistical and computational guarantees ⋮ Randomized incomplete \(U\)-statistics in high dimensions ⋮ Gaussian approximations for high-dimensional non-degenerate \(U\)-statistics via exchangeable pairs ⋮ Improved central limit theorem and bootstrap approximations in high dimensions ⋮ On the consistency of incomplete U-statistics under infinite second-order moments ⋮ Stratified incomplete local simplex tests for curvature of nonparametric multiple regression
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Measuring and testing dependence by correlation of distances
- Large-Sample Theory for the Bergsma-Dassios Sign Covariance
- Asymptotic distributions of weighted \(U\)-statistics of degree 2
- Asymptotic distribution of symmetric statistics
- Asymptotic normality of permutation statistics derived from weighted sums of bivariate functions
- Some asymptotic theory for the bootstrap
- On the bootstrap of \(U\) and \(V\) statistics
- Generalized bootstrap for studentized U-statistics: A rank statistic approach
- Reduced U-statistics and the Hodges-Lehmann estimator
- Consistency of the generalized bootstrap for degenerate \(U\)-statistics
- Random quadratic forms and the bootstrap for \(U\)-statistics
- Asymptotic distributions for weighted \(U\)-statistics
- Testing independence in high dimensions with sums of rank correlations
- Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications
- On weighted \(U\)-statistics for stationary processes.
- Weighted bootstrap for \(U\)-statistics
- Weak convergence and empirical processes. With applications to statistics
- Jackknife multiplier bootstrap: finite sample approximations to the \(U\)-process supremum with applications
- Randomized incomplete \(U\)-statistics in high dimensions
- Central limit theorems and bootstrap in high dimensions
- Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
- A consistent test of independence based on a sign covariance related to Kendall's tau
- Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
- Pairwise Independence of Jointly Dependent Variables
- The asymptotic distributions of incomplete U-statistics
- Some properties of incomplete U-statistics
- Asymptotic Statistics
- Testing Mutual Independence in High Dimension via Distance Covariance
- A Scalable Bootstrap for Massive Data
- Distribution-free tests of independence in high dimensions
- Incomplete Generalized U‐Statistics for Food Risk Assessment
- A Class of Statistics with Asymptotically Normal Distribution
- A Non-Parametric Test of Independence
- On the asymptotic behavior of weighted \(U\)-statistics
This page was built for publication: Randomized incomplete \(U\)-statistics in high dimensions