Auto-association measures for stationary time series of categorical data

Atanu Biswas (), Maria Carmen Pardo () and Apratim Guha ()

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2014, vol. 23, issue 3, 487-514

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. Copyright Sociedad de Estadística e Investigación Operativa 2014

Keywords: Power divergence; Havrda–Charvat entropy; ARMA; Categorical data analysis; Auto-association; 62M10; 62H10; 60G10 (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s11749-014-0364-8

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