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dc.contributor.authorMantalos, Panagiotisen
dc.contributor.authorMattheou, K.en
dc.contributor.authorKaragrigoriou, Alexen
dc.creatorMantalos, Panagiotisen
dc.creatorMattheou, K.en
dc.creatorKaragrigoriou, Alexen
dc.description.abstractIn this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an asymptotically unbiased estimator of the expected overall discrepancy, a nonnegative quantity that measures the distance between the true unknown model and a fitted approximating model. Further, we use Monte Carlo methods and various data generating processes for small, medium, and large sample sizes in order to explore the capabilities of the new criterion in selecting the optimal order in autoregressive processes and in general in a time series context. The new criterion shows remarkably good results by choosing the correct model more frequently than traditional information criteria. © Taylor & Francis Group, LLC.en
dc.sourceCommunications in Statistics: Simulation and Computationen
dc.subjectMonte Carlo methodsen
dc.subjectTime seriesen
dc.subjectModel selectionen
dc.subjectSample sizesen
dc.subjectAR processen
dc.subjectAuto regressive processen
dc.subjectInformation criterionen
dc.subjectMeasure of divergenceen
dc.subjectProcess informationen
dc.subjectUnbiased estimatoren
dc.titleAn improved divergence information criterion for the determination of the order of an ar processen
dc.description.endingpage879Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Μαθηματικών και Στατιστικής / Department of Mathematics and Statistics
dc.description.notes<p>Cited By :2</p>en
dc.contributor.orcidKaragrigoriou, Alex [0000-0002-4919-2133]
dc.contributor.orcidMantalos, Panagiotis [0000-0002-8425-3990]

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