We discuss how MultiFIT, the Multiscale Fisher’s Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence criterion (HSIC). We highlight the fact that the levels of the kernel tests at any finite sample size can be controlled exactly, as it is the case with the level of MultiFIT. In our experiments, we observe some of the performance limitations of MultiFIT in terms of test power
Test of independence is of fundamental importance in modern data analysis, with broad applications i...
This thesis contributes to the field of nonparametric hypothesis testing (i.e. two-sample and indepe...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...
We discuss how MultiFIT, the Multiscale Fisher’s Independence Test for Multivariate Dependence propo...
8 pagesWe discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependenc...
Invited discussion for Biometrika of 'Multivariate Fisher's independence test for multivariate depen...
Although kernel measures of independence have been widely applied in machine learning (notably in ke...
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
Dependence measures based on reproducing kernel Hilbert spaces, also known as Hilbert-Schmidt Indepe...
International audienceA new computationally efficient dependence measure, and an adaptive statistica...
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible fram...
We propose an independence criterion based on the eigenspectrum of covariance operators in reproduci...
The Hilbert-Schmidt Independence Criterion (HSIC) is a dependence measure based on reproducing kerne...
Test of independence is of fundamental importance in modern data analysis, with broad applications i...
This thesis contributes to the field of nonparametric hypothesis testing (i.e. two-sample and indepe...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...
We discuss how MultiFIT, the Multiscale Fisher’s Independence Test for Multivariate Dependence propo...
8 pagesWe discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependenc...
Invited discussion for Biometrika of 'Multivariate Fisher's independence test for multivariate depen...
Although kernel measures of independence have been widely applied in machine learning (notably in ke...
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
Dependence measures based on reproducing kernel Hilbert spaces, also known as Hilbert-Schmidt Indepe...
International audienceA new computationally efficient dependence measure, and an adaptive statistica...
Representations of probability measures in reproducing kernel Hilbert spaces provide a flexible fram...
We propose an independence criterion based on the eigenspectrum of covariance operators in reproduci...
The Hilbert-Schmidt Independence Criterion (HSIC) is a dependence measure based on reproducing kerne...
Test of independence is of fundamental importance in modern data analysis, with broad applications i...
This thesis contributes to the field of nonparametric hypothesis testing (i.e. two-sample and indepe...
Three simple and explicit procedures for testing the independence of two multi-dimensional random va...