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Auto-association measures for stationary time series of categorical data.

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Biswas, Atanu and Pardo Llorente, María del Carmen (2014) Auto-association measures for stationary time series of categorical data. Test, 23 (3). pp. 487-514. ISSN 1133-0686

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Official URL: http://link.springer.com/article/10.1007%2Fs11749-014-0364-8


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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.


Item Type:Article
Uncontrolled Keywords:Power divergence; Havrda–Charvat entropy; ARMA Categorical data analysis; Auto-association
Subjects:Sciences > Mathematics > Mathematical statistics
ID Code:37278
Deposited On:28 Apr 2016 10:26
Last Modified:19 Feb 2019 15:15

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