Publication: Auto-association measures for stationary time series
of categorical data.
Loading...
Full text at PDC
Publication Date
2014
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Citations
Exportar
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.