Auto-association measures for stationary time series of categorical data

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DC Field	Value	Language
dc.contributor.author	Biswas, Atanu
dc.contributor.author	Del Carmen Pardo, Maria
dc.contributor.author	Guha, Apratim
dc.date.accessioned	2015-05-05T13:59:37Z
dc.date.available	2015-05-05T13:59:37Z
dc.date.issued	2014
dc.identifier.citation	Biswas, A., del Carmen Pardo, M., & Guha, A. (2014). Auto-association Measures for Stationary Time Series of Categorical Data. Test, 23(3), 487-514.	en_US
dc.identifier.issn	11330686
dc.identifier.uri	http://hdl.handle.net/11718/13444
dc.description.abstract	For stationary time series of nominal categorical data or ordinal categorical data (with arbitrary ordered numberings of the categories), autocorrelation does not make much sense. Biswas and Guha (J Stat Plan Infer 139:3076-3087, 2009a) used mutual information as a measure of association and introduced the concept of auto-mutual information in this context. In this present paper, we introduce general auto-association measures for this purpose and study several special cases. Theoretical properties and simulation results are given along with two illustrative real data examples.	en_US
dc.language.iso	en	en_US
dc.publisher	TEST	en_US
dc.subject	Autocorrelation	en_US
dc.subject	Time Series	en_US
dc.title	Auto-association measures for stationary time series of categorical data	en_US
dc.type	Article	en_US
Appears in Collections:	Journal Articles

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