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Autores principales: Ye Liu, Dehui Wang
Formato: Artículo Open Access
Publicado: Wiley 2024
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Acceso en línea:https://onlinelibrary.wiley.com/doi/10.1002/sta4.721
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author Ye Liu
Dehui Wang
author_facet Ye Liu
Dehui Wang
Ye Liu
Dehui Wang
collection Wiley Open Access
contents A novel time‐varying coefficient Poisson difference model driven by observation Ye Liu Dehui Wang Stat AbstractThis paper studies a novel time‐varying coefficient integer‐valued time series model driven by observation. The model is suitable for modeling negative integer‐valued time series based on the Poisson difference distribution and extended binomial thinning operator. Main methods used to estimate the parameters are the conditional least squares (CLS) and conditional maximum likelihood (CML) methods. This paper also discusses the consistency and asymptotic normality of the estimation results. Likelihood ratio tests are employed to examine the existence of covariate and observation. Numerical simulations are conducted to verify the accuracy and stability of the model. Finally, a real data application is presented to demonstrate the usefulness and adaptability of this newly proposed model. 10.1002/sta4.721 http://onlinelibrary.wiley.com/termsAndConditions#vor
doi_str_mv 10.1002/sta4.721
format Artículo Open Access
id wiley_oa_10_1002_sta4_721
institution Wiley Open Access
license_str_mv http://onlinelibrary.wiley.com/termsAndConditions#vor
publishDate 2024
publisher Wiley
record_format wiley_oa
spellingShingle A novel time‐varying coefficient Poisson difference model driven by observation
Ye Liu
Dehui Wang
Stat
A novel time‐varying coefficient Poisson difference model driven by observation Ye Liu Dehui Wang Stat AbstractThis paper studies a novel time‐varying coefficient integer‐valued time series model driven by observation. The model is suitable for modeling negative integer‐valued time series based on the Poisson difference distribution and extended binomial thinning operator. Main methods used to estimate the parameters are the conditional least squares (CLS) and conditional maximum likelihood (CML) methods. This paper also discusses the consistency and asymptotic normality of the estimation results. Likelihood ratio tests are employed to examine the existence of covariate and observation. Numerical simulations are conducted to verify the accuracy and stability of the model. Finally, a real data application is presented to demonstrate the usefulness and adaptability of this newly proposed model. 10.1002/sta4.721 http://onlinelibrary.wiley.com/termsAndConditions#vor
title A novel time‐varying coefficient Poisson difference model driven by observation
topic Stat
url https://onlinelibrary.wiley.com/doi/10.1002/sta4.721