• Corpus ID: 210157239

Multiscale Fisher's Independence Test for Multivariate Dependence

  title={Multiscale Fisher's Independence Test for Multivariate Dependence},
  author={Shai Gorsky and Li Ma},
  journal={arXiv: Methodology},
  • S. Gorsky, Li Ma
  • Published 18 June 2018
  • Computer Science, Mathematics
  • arXiv: Methodology
Identifying dependency in multivariate data is a common inference task that arises in numerous applications. However, existing nonparametric independence tests typically require computation that scales at least quadratically with the sample size, making it difficult to apply them to massive data. Moreover, resampling is usually necessary to evaluate the statistical significance of the resulting test statistics at finite sample sizes, further worsening the computational burden. We introduce a… 
Discussion of Multiscale Fisher's Independence Test for Multivariate Dependence
Independence Test for Multivariate Dependence’ Duyeol Lee, Helal El-Zaatari, Michael R. Kosorok, Xinyi Li, and Kai Zhang Corporate Risk, Wells Fargo, McLean, VA 22102 Department of Biostatistics,
Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'
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
Comments on “A Gibbs Sampler for a Class of Random Convex Polytopes”
In this comment we discuss relative strengths and weaknesses of simplex and Dirichlet Dempster-Shafer inference as applied to multi-resolution tests of independence.
The BEAST, a BEAST that improves the empirical power of many existing tests against a wide spectrum of common alternatives while providing clear interpretation of the form of non-uniformity upon rejection, is developed.


Testing Independence with the Binary Expansion Randomized Ensemble Test
A new test by an ensemble method that uses the sum of squared symmetry statistics and distance correlation to test the independence of two continuous random variables in arbitrary dimension is developed.
On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives
The asymptotic properties of goodness-of-fit, homogeneity and independence tests using Gaussian kernels, arguably the most popular and successful among such tests, are studied to provide theoretical justifications for this common practice.
A Simple Sequentially Rejective Multiple Test Procedure
This paper presents a simple and widely ap- plicable multiple test procedure of the sequentially rejective type, i.e. hypotheses are rejected one at a tine until no further rejections can be done. It
Asymptotics of multivariate contingency tables with fixed marginals
  • Quan Zhou
  • Mathematics
    Journal of Statistical Planning and Inference
  • 2019
Nonconservative exact small-sample inference for discrete data
Symmetric rank covariances: a generalized framework for nonparametric measures of dependence
Symmetric Rank Covariances is a new class of multivariate nonparametric measures of dependence that generalises all of the above measures and leads naturally to multivariate extensions of the Bergsma--Dassios sign covariance.
Fisher Exact Scanning for Dependency
  • Li Ma, Jialiang Mao
  • Computer Science, Mathematics
    Journal of the American Statistical Association
  • 2018
It is shown that there exists a coarse-to-fine, sequential generative representation for the MHG model in the form of a Bayesian network, which implies the mutual independence among the Fisher’s exact tests completed under FES.
Kernel-based Tests for Joint Independence
This work embeds the joint distribution and the product of the marginals in a reproducing kernel Hilbert space and defines the d‐variable Hilbert–Schmidt independence criterion dHSIC as the squared distance between the embeddings.