New upper bounds for tight and fast approximation of Fisher's exact test in dependency rule mining

DOI10.1016/j.csda.2015.08.002zbMath1468.62072OpenAlexW1160788859MaRDI QIDQ1660238

Wilhelmiina Hämäläinen

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.08.002

zbMATH Keywords

upper boundapproximationdata mining\(p\)-valueFisher's exact testhypergeometric distributiondependency rule

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Contingency tables (62H17)

Related Items (3)

2nd special issue on robust analysis of complex data ⋮ Editorial: Special issue on advances in data mining and robust statistics ⋮ A tutorial on statistically sound pattern discovery

Uses Software

• UCI-ml
• gprof

Cites Work

• Unnamed Item
• A Network Algorithm for Performing Fisher's Exact Test in r × c Contingency Tables
• Comparing the asymptotic power of exact tests in \(2\times 2\) tables
• A major improvement to the network algorithm for Fisher's exact test in \(2\times c\) contingency tables
• A survey of algorithms for exact distributions of test statistics in r\(\times c\) contingency tables with fixed margins
• A survey of exact inference for contingency tables. With comments and a rejoinder by the author
• A network algorithm for the exact treatment of the 2×k contingency table
• An accurate computation of the hypergeometric distribution function

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