Comparing a large number of multivariate distributions
From MaRDI portal
Publication:2214253
DOI10.3150/20-BEJ1244zbMath1467.62065arXiv1904.05741MaRDI QIDQ2214253
Publication date: 7 December 2020
Published in: Bernoulli (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1904.05741
Multivariate distribution of statistics (62H10) Nonparametric hypothesis testing (62G10) Asymptotic distribution theory in statistics (62E20) Inequalities; stochastic orderings (60E15) Hypothesis testing in multivariate analysis (62H15)
Related Items (2)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Equivalence of distance-based and RKHS-based statistics in hypothesis testing
- Permutation p-value approximation via generalized Stolarsky invariance
- High-dimensional sparse MANOVA
- Cramér-von Mises and characteristic function tests for the two and \(k\)-sample problems with dependent data
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
- The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
- \(k\)-sample test based on the common area of kernel density estimators
- DISCO analysis: A nonparametric extension of analysis of variance
- Comparing distributions
- Two moments suffice for Poisson approximations: The Chen-Stein method
- On the asymptotic power of some k-sample statistics based on the multivariate empirical process
- On a new multivariate two-sample test.
- Concentration of normalized sums and a central limit theorem for noncorrelated random variables
- Concentration inequalities for randomly permuted sums
- On convergence rates of suprema
- Sign-based test for mean vector in high-dimensional and sparse settings
- Stein's method for concentration inequalities
- \(K\)-sample problem using strong approximations of empirical copula processes
- Circular law for random matrices with exchangeable entries
- Testing equality of a large number of densities
- Tests for the multivariatek-sample problem based on the empirical characteristic function
- Kernel Mean Embedding of Distributions: A Review and Beyond
- Power Enhancement in High-Dimensional Cross-Sectional Tests
- Minimax Estimation of Kernel Mean Embeddings
- High-Dimensional Probability
- Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
- Two-Sample Test of High Dimensional Means Under Dependence
- A nonparametric approach to high-dimensional k-sample comparison problems
- Optimal Sparse Segment Identification With Application in Copy Number Variation Analysis
- K-Sample Analogues of the Kolmogorov-Smirnov and Cramer-V. Mises Tests
- NonparametricK-Sample Tests via Dynamic Slicing
- Distribution-free tests of independence in high dimensions
- Universality, Characteristic Kernels and RKHS Embedding of Measures
- Testing Statistical Hypotheses
- Several $k$-Sample Kolmogorov-Smirnov Tests
- Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
This page was built for publication: Comparing a large number of multivariate distributions