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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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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. |
format | Online Article Text |
id | pubmed-10115473 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
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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