Some applications of Kullback-Leibler and Jeffreys’ divergences in multinomial populations

dc.authorid0000-0003-4094-7664en_US
dc.contributor.authorEvren, Atif Ahmet
dc.date.accessioned2020-12-21T08:37:46Z
dc.date.available2020-12-21T08:37:46Z
dc.date.issued2012en_US
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSome of the entropy measures proposed are Shannon entropy(1948), Rényi entropy (1961), Havrda&Charvát entropy(1967), and Tsallis entropy (1988). The limit of Rényi divergence is relative entropy (or Kullback-Leibler divergence) which is a measure of discrepancy between two statistical hypotheses or two probability distributions. Jeffreys’ divergence is a measure of difficulty of making a discrimination between two probability distributions. These divergence measures are related to some chi-square distributions asymptotically such that they can be used in some hypothesis tests. In this study I try to show that entropy based statistics like Kullback-Leibler divergence and Jeffreys’ divergence can be used in some statistical hypothesis tests for multinomial populations by some examples.en_US
dc.identifier.citationEvren, A. A. (2012). Some applications of Kullback-Leibler and Jeffreys’ divergences in multinomial populations. Journal of Selcuk University Natural and Applied Science, 1, (4), 48-58.en_US
dc.identifier.endpage58en_US
dc.identifier.issn2147-3781en_US
dc.identifier.issue4en_US
dc.identifier.startpage48en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/40863
dc.identifier.volume1en_US
dc.language.isoenen_US
dc.publisherSelçuk Üniversitesien_US
dc.relation.ispartofJournal of Selcuk University Natural and Applied Scienceen_US
dc.relation.publicationcategoryMakale - Ulusal - Editör Denetimli Dergien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectGoodness of fiten_US
dc.subjectJeffreys’ divergenceen_US
dc.subjectKullback-Leibler divergenceen_US
dc.subjectShannon entropyen_US
dc.titleSome applications of Kullback-Leibler and Jeffreys’ divergences in multinomial populationsen_US
dc.typeArticleen_US

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