Scalable kernel two-sample tests via empirical likelihood and jackknife

DOI10.1080/03610918.2021.2005096OpenAlexW3215122718MaRDI QIDQ6204959

Mingao Yuan, Qian Wen

Publication date: 11 April 2024

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2021.2005096

zbMATH Keywords

empirical likelihoodjackknifetwo-sample testdivide-and-conquermaximum mean discrepancy

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests
• Some asymptotic theory for the bootstrap
• The jackknife estimate of variance
• Empirical likelihood and general estimating equations
• Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates
• Divide and conquer in nonstandard problems and the super-efficiency phenomenon
• Empirical likelihood for non-degenerate \(U\)-statistics
• On the Asymptotic Properties of a Nonparametric<tex>$L_1$</tex>-Test Statistic of Homogeneity
• Empirical likelihood ratio confidence intervals for a single functional
• Permutation tests for equality of distributions in high-dimensional settings
• Empirical likelihood test for equality of two distributions using distance of characteristic functions
• Kernel Mean Embedding of Distributions: A Review and Beyond
• Table for Estimating the Goodness of Fit of Empirical Distributions

This page was built for publication: Scalable kernel two-sample tests via empirical likelihood and jackknife