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Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process

In this paper, the authors consider the empirical likelihood method for a first-order generalized random coefficient integer-valued autoregressive process. The authors establish the log empirical likelihood ratio statistic and obtain its limiting distribution. Furthermore, the authors investigate th...

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Detalles Bibliográficos
Autores principales: Cheng, Jianhua, Wang, Xu, Wang, Dehui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115473/
https://www.ncbi.nlm.nih.gov/pubmed/37095761
http://dx.doi.org/10.1007/s11424-023-1051-1
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author Cheng, Jianhua
Wang, Xu
Wang, Dehui
author_facet Cheng, Jianhua
Wang, Xu
Wang, Dehui
author_sort Cheng, Jianhua
collection PubMed
description In this paper, the authors consider the empirical likelihood method for a first-order generalized random coefficient integer-valued autoregressive process. The authors establish the log empirical likelihood ratio statistic and obtain its limiting distribution. Furthermore, the authors investigate the point estimation, confidence regions and hypothesis testing for the parameters of interest. The performance of empirical likelihood method is illustrated by a simulation study and a real data example.
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spelling pubmed-101154732023-04-20 Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process Cheng, Jianhua Wang, Xu Wang, Dehui J Syst Sci Complex Article In this paper, the authors consider the empirical likelihood method for a first-order generalized random coefficient integer-valued autoregressive process. The authors establish the log empirical likelihood ratio statistic and obtain its limiting distribution. Furthermore, the authors investigate the point estimation, confidence regions and hypothesis testing for the parameters of interest. The performance of empirical likelihood method is illustrated by a simulation study and a real data example. Springer Berlin Heidelberg 2023-04-19 2023 /pmc/articles/PMC10115473/ /pubmed/37095761 http://dx.doi.org/10.1007/s11424-023-1051-1 Text en © The Editorial Office of JSSC & Springer-Verlag GmbH Germany 2023 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 Article
Cheng, Jianhua
Wang, Xu
Wang, Dehui
Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process
title Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process
title_full Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process
title_fullStr Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process
title_full_unstemmed Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process
title_short Empirical Likelihood for a First-Order Generalized Random Coefficient Integer-Valued Autoregressive Process
title_sort empirical likelihood for a first-order generalized random coefficient integer-valued autoregressive process
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115473/
https://www.ncbi.nlm.nih.gov/pubmed/37095761
http://dx.doi.org/10.1007/s11424-023-1051-1
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AT wangdehui empiricallikelihoodforafirstordergeneralizedrandomcoefficientintegervaluedautoregressiveprocess