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Monitoring parameter change for bivariate time series models of counts

In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum...

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Detalles Bibliográficos
Autores principales: Lee, Sangyeol, Kim, Dongwon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164370/
https://www.ncbi.nlm.nih.gov/pubmed/37361425
http://dx.doi.org/10.1007/s42952-023-00212-9
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author Lee, Sangyeol
Kim, Dongwon
author_facet Lee, Sangyeol
Kim, Dongwon
author_sort Lee, Sangyeol
collection PubMed
description In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum (CUSUM) process constructed from the (standardized) residuals obtained from those models. To attain control limits, we develop limit theorems for the proposed monitoring process. A simulation study and real data analysis are conducted to affirm the validity of the proposed method.
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spelling pubmed-101643702023-05-09 Monitoring parameter change for bivariate time series models of counts Lee, Sangyeol Kim, Dongwon J Korean Stat Soc Research Article In this study, we consider an online monitoring procedure to detect a parameter change for bivariate time series of counts, following bivariate integer-valued generalized autoregressive heteroscedastic (BIGARCH) and autoregressive (BINAR) models. To handle this problem, we employ the cumulative sum (CUSUM) process constructed from the (standardized) residuals obtained from those models. To attain control limits, we develop limit theorems for the proposed monitoring process. A simulation study and real data analysis are conducted to affirm the validity of the proposed method. Springer Nature Singapore 2023-05-07 /pmc/articles/PMC10164370/ /pubmed/37361425 http://dx.doi.org/10.1007/s42952-023-00212-9 Text en © Korean Statistical Society 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Research Article
Lee, Sangyeol
Kim, Dongwon
Monitoring parameter change for bivariate time series models of counts
title Monitoring parameter change for bivariate time series models of counts
title_full Monitoring parameter change for bivariate time series models of counts
title_fullStr Monitoring parameter change for bivariate time series models of counts
title_full_unstemmed Monitoring parameter change for bivariate time series models of counts
title_short Monitoring parameter change for bivariate time series models of counts
title_sort monitoring parameter change for bivariate time series models of counts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164370/
https://www.ncbi.nlm.nih.gov/pubmed/37361425
http://dx.doi.org/10.1007/s42952-023-00212-9
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