A regression perspective on generalized distance covariance and the Hilbert-Schmidt independence criterion

DOI10.1214/21-STS841OpenAlexW4306158317MaRDI QIDQ2092898

Dominic Edelmann, Jelle J. Goeman

Publication date: 4 November 2022

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/journals/statistical-science/volume-37/issue-4/A-Regression-Perspective-on-Generalized-Distance-Covariance-and-the-HilbertSchmidt/10.1214/21-STS841.full

zbMATH Keywords

equivalencedistance covariancedistance correlationlocally most powerfulHilbert-Schmidt independence criterionglobal test

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

A nonparametric test for comparing survival functions based on restricted distance correlation

Uses Software

• energy
• globaltest

Cites Work

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• Partial distance correlation with methods for dissimilarities
• Measuring and testing dependence by correlation of distances
• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• The distance standard deviation
• Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control
• Distance covariance in metric spaces
• Energy statistics: a class of statistics based on distances
• The distance correlation \(t\)-test of independence in high dimension
• Mercer's theorem on general domains: on the interaction between measures, kernels, and RKHSs
• The affinely invariant distance correlation
• Mercer theorem for RKHS on noncompact sets
• Distance correlation coefficients for Lancaster distributions
• Kernel methods in machine learning
• Brownian distance covariance
• DISCO analysis: A nonparametric extension of analysis of variance
• On a new multivariate two-sample test.
• Least angle regression. (With discussion)
• The exact equivalence of distance and kernel methods in hypothesis testing
• Detecting independence of random vectors: generalized distance covariance and Gaussian covariance
• A test for the mean vector with fewer observations than the dimension
• Equivalence of kernel machine regression and kernel distance covariance for multidimensional phenotype association studies
• A global × global test for testing associations between two large sets of variables
• Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction
• 10.1162/15324430260185556
• Testing Against a High Dimensional Alternative
• Rotation‐based multiple testing in the multivariate linear model
• Kernel-Based Tests for Joint Independence
• Testing the Fit of a Regression Model Via Score Tests in Random Effects Models
• Robust Testing in Generalized Linear Models by Sign Flipping Score Contributions
• A Goodness‐of‐Fit Test for Multinomial Logistic Regression
• Mercer’s Theorem, Feature Maps, and Smoothing
• Regularization and Variable Selection Via the Elastic Net
• Conditional Distance Correlation
• Tests for High Dimensional Generalized Linear Models
• Algorithmic Learning Theory
• Application of the Method of Mixtures to Quadratic Forms in Normal Variates
• Discussion of: Brownian distance covariance
• An Updated Literature Review of Distance Correlation and Its Applications to Time Series

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