Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'

Autor:	Schrab, Antonin, Jitkrittum, Wittawat, Szabó, Zoltán, Sejdinovic, Dino, Gretton, Arthur
Rok vydání:	2022
Předmět:	Statistics - Methodology Computer Science - Machine Learning Statistics - Applications Statistics - Computation Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 8 pages
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.11142 Zobrazit plný text záznamu View this record from Arxiv