scientific article; zbMATH DE number 7370518
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Publication:4998859
Ming Yuan, Krishnakumar Balasubramanian, Tong Li
Publication date: 9 July 2021
Full work available at URL: https://arxiv.org/abs/1709.08148
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
adaptationreproducing kernel Hilbert spaceoptimal rates of convergencegoodness of fitmaximum mean discrepancy
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Cites Work
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- Measuring and testing dependence by correlation of distances
- Equivalence of distance-based and RKHS-based statistics in hypothesis testing
- The two-sample problem for Poisson processes: adaptive tests with a nonasymptotic wild bootstrap approach
- Distance covariance in metric spaces
- On combinatorial testing problems
- Central limit theorem for integrated square error of multivariate nonparametric density estimators
- Sobolev tests of goodness of fit of distributions on compact Riemannian manifolds
- On the rate of convergence in the central limit theorem for martingales with discrete and continuous time
- Exact mean integrated squared error
- Large sample theory for U-statistics and tests of fit
- Minimax nonparametric hypothesis testing: The case of an inhomogeneous alternative
- Asymptotically minimax hypothesis testing for nonparametric alternatives. I
- Adaptive hypothesis testing using wavelets
- Adaptive chi-square tests
- Nonparametric goodness-of-fit testing under Gaussian models
- Non-asymptotic minimax rates of testing in signal detection
- The multivariate Watson distribution: maximum-likelihood estimation and other aspects
- A one-sample test for normality with kernel methods
- Adaptive goodness-of-fit tests in a density model
- 10.1162/153244302760185252
- Hilbert space embeddings and metrics on probability measures
- Learning Theory
- Support Vector Machines
- A Hilbert Space Embedding for Distributions
- Minimax Testing of Nonparametric Hypotheses on a Distribution Density in the $L_p$ Metrics
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Minimax nonparametric hypothesis testing for ellipsoids and Besov bodies
- Kernel Mean Embedding of Distributions: A Review and Beyond
- An Extension of MATLAB to Continuous Functions and Operators
- Minimax Detection of a Signal In a Gaussian White Noise
- Mercer’s Theorem, Feature Maps, and Smoothing
- On the Equivalence between Kernel Quadrature Rules and Random Feature Expansions
- Universality, Characteristic Kernels and RKHS Embedding of Measures
- Testing Statistical Hypotheses
- Theory of Reproducing Kernels
- Aggregating inconsistent information
- Introduction to nonparametric estimation