Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jspi.2009.02.009
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dc.titleTime series analysis of categorical data using auto-mutual information
dc.contributor.authorBiswas, A.
dc.contributor.authorGuha, A.
dc.date.accessioned2014-10-28T05:16:04Z
dc.date.available2014-10-28T05:16:04Z
dc.date.issued2009-09-01
dc.identifier.citationBiswas, 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
dc.identifier.issn03783758
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105435
dc.description.abstractDespite 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jspi.2009.02.009
dc.sourceScopus
dc.subjectAuto-correlation function
dc.subjectMaximum likelihood estimates
dc.subjectMixture distribution
dc.subjectMutual information
dc.subjectPartial auto-correlation function
dc.subjectThinning operator
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.jspi.2009.02.009
dc.description.sourcetitleJournal of Statistical Planning and Inference
dc.description.volume139
dc.description.issue9
dc.description.page3076-3087
dc.description.codenJSPID
dc.identifier.isiut000267956600018
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