An empirical study of the maximal and total information coefficients and leading measures of dependence

DOI10.1214/17-AOAS1093zbMath1393.62094WikidataQ130148962 ScholiaQ130148962MaRDI QIDQ1647596

Yakir A. Reshef, Pardis C. Sabeti, David N. Reshef, Michael Mitzenmacher

Publication date: 26 June 2018

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1520564467

zbMATH Keywords

measures of dependencestatistical powerequitabilitymaximal information coefficienttotal information coefficient

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Related Items (10)

Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs ⋮ On a multivariate copula-based dependence measure and its estimation ⋮ An empirical study of the maximal and total information coefficients and leading measures of dependence ⋮ Fast search local extremum for maximal information coefficient (MIC) ⋮ Multivariate Rank-Based Distribution-Free Nonparametric Testing Using Measure Transportation ⋮ Equitability, interval estimation, and statistical power ⋮ Estimating scale-invariant directed dependence of bivariate distributions ⋮ Validation of association ⋮ Statistical dependence: beyond Pearson's \(\rho\) ⋮ A method for extracting nonlinear structure based on measures of dependence

Uses Software

• HHG

Cites Work

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• Equivalence of distance-based and RKHS-based statistics in hypothesis testing
• Detecting Novel Associations in Large Data Sets
• A class of multivariate distribution-free tests of independence based on graphs
• Brownian distance covariance
• An empirical study of the maximal and total information coefficients and leading measures of dependence
• Measuring dependence powerfully and equitably
• Equitability, mutual information, and the maximal information coefficient
• An informational measure of correlation
• On measures of dependence
• Estimating Optimal Transformations for Multiple Regression and Correlation
• Estimation of Entropy and Mutual Information
• NonparametricK-Sample Tests via Dynamic Slicing
• Generalized R-squared for detecting dependence
• Algorithmic Learning Theory
• A consistent multivariate test of association based on ranks of distances
• A Non-Parametric Test of Independence

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