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dc.contributor.authorYenigün, C. D.en_US
dc.contributor.authorSzékely, G. J.en_US
dc.contributor.authorRizzo, M. L.en_US
dc.date.accessioned2016-02-08T09:54:56Z
dc.date.available2016-02-08T09:54:56Z
dc.date.issued2011en_US
dc.identifier.issn0361-0926
dc.identifier.urihttp://hdl.handle.net/11693/22060
dc.description.abstractMaximal correlation has several desirable properties as a measure of dependence, including the fact that it vanishes if and only if the variables are independent. Except for a few special cases, it is hard to evaluate maximal correlation explicitly. We focus on two-dimensional contingency tables and discuss a procedure for estimating maximal correlation, which we use for constructing a test of independence. We compare the maximal correlation test with other tests of independence by Monte Carlo simulations. When the underlying continuous variables are dependent but uncorrelated, we point out some cases for which the new test is more powerful.en_US
dc.language.isoEnglishen_US
dc.source.titleCommunications in Statistics - Theory and Methodsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/03610921003764274en_US
dc.subjectExact testsen_US
dc.subjectMaximal correlationen_US
dc.subjectTests of independenceen_US
dc.subjectContingency tableen_US
dc.subjectContinuous variablesen_US
dc.subjectExact testsen_US
dc.subjectMaximal correlationen_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectTests of independenceen_US
dc.subjectComputer simulationen_US
dc.subjectCorrelation methodsen_US
dc.subjectMonte Carlo methodsen_US
dc.subjectTestingen_US
dc.titleA test of independence in two-way contingency tables based on maximal correlationen_US
dc.typeArticleen_US
dc.departmentDepartment of Managementen_US
dc.citation.spage2225en_US
dc.citation.epage2242en_US
dc.citation.volumeNumber40en_US
dc.citation.issueNumber12en_US
dc.identifier.doi10.1080/03610921003764274en_US
dc.publisherTaylor & Francisen_US


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