Using balanced iterative reducing and clustering hierarchies to compute approximate rank statistics on massive datasets

DOI10.1080/00949655.2013.787534zbMath1453.62528OpenAlexW2025320472MaRDI QIDQ5219487

Jean-François Plante, Lysiane Charest

Publication date: 12 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2013.787534

zbMATH Keywords

correlationrank statisticsKendall's \(\tau \)Spearman's \(\rho \)massive datasetBIRCH

Mathematics Subject Classification ID

Nonparametric estimation (62G05) Measures of association (correlation, canonical correlation, etc.) (62H20)

Uses Software

• UCI-ml
• copula
• TwoCop
• copula
• birch

Cites Work

• An introduction to copulas.
• Finding approximate solutions to combinatorial problems with very large data sets using BIRCH
• Testing for equality between two copulas
• On nonparametric measures of dependence for random variables
• A semiparametric estimation procedure of dependence parameters in multivariate families of distributions
• A Primer on Copulas for Count Data
• A NEW MEASURE OF RANK CORRELATION
• Rank Correlation when There are Equal Variates
• The Estimation and Comparison of Strengths of Association in Contingency Tables
• THE TREATMENT OF TIES IN RANKING PROBLEMS
• Unnamed Item

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