Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2009.02.009
Title: Time series analysis of categorical data using auto-mutual information
Authors: Biswas, A.
Guha, A. 
Keywords: Auto-correlation function
Maximum likelihood estimates
Mixture distribution
Mutual information
Partial auto-correlation function
Thinning operator
Issue Date: 1-Sep-2009
Citation: Biswas, A., Guha, A. (2009-09-01). Time series analysis of categorical data using auto-mutual information. Journal of Statistical Planning and Inference 139 (9) : 3076-3087. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jspi.2009.02.009
Abstract: Despite its importance, there has been little attention in the modeling of time series data of categorical nature in the recent past. In this paper, we present a framework based on the Pegram's [An autoregressive model for multilag Markov chains. Journal of Applied Probabability 17, 350-362] operator that was originally proposed only to construct discrete AR(p) processes. We extend the Pegram's operator to accommodate categorical processes with ARMA representations. We observe that the concept of correlation is not always suitable for categorical data. As a sensible alternative, we use the concept of mutual information, and introduce auto-mutual information to define the time series process of categorical data. Some model selection and inferential aspects are also discussed. We implement the developed methodologies to analyze a time series data set on infant sleep status. © 2008 Elsevier B.V. All rights reserved.
Source Title: Journal of Statistical Planning and Inference
URI: http://scholarbank.nus.edu.sg/handle/10635/105435
ISSN: 03783758
DOI: 10.1016/j.jspi.2009.02.009
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