Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/23775
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dc.contributor.authorBiswas, Atanu-
dc.contributor.authorPardo, Maria del Carmen-
dc.date.accessioned2021-03-26T12:17:36Z-
dc.date.available2021-03-26T12:17:36Z-
dc.date.issued2013-05-
dc.identifier.urihttp://hdl.handle.net/11718/23775-
dc.description.abstractFor stationary time series of nominal categorical data or ordinal categorical data (with arbitrary ordered numberings of the categories), autocorrelation does not make much sense. One can alternatively think of using some entropic measures, of which a measure introduced by Havrda and Charvat (1967) could be particularly useful. We discuss some theoretical properties of measures from this class in the context of categorical time series and look at specific examples. Theoretical properties and simulation results are given along with an illustrative real data example.en_US
dc.language.isoenen_US
dc.subjectauto-havraen_US
dc.subjectentropicen_US
dc.subjectstationary timeen_US
dc.titleAuto-Havra Charvat entropic measures for stationary time series of categorical dataen_US
dc.typeWorking Paperen_US
Appears in Collections:Working Papers

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