A new improvement Liu-type estimator for the Bell regression model

The Poisson Regression Model (PRM) is a well-known model in applications when the response variable consists of count data. However, Bell Regression Model (BRM) is proposed recently as an alternative to the PRM in some cases where the data is over-dispersed. But, multicollinearity between explanator...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 54; no. 3; pp. 603 - 614
Main Authors Ertan, Esra, Algamal, Zakariya Yahya, Erkoç, Ali, Akay, Kadri Ulaş
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 04.03.2025
Taylor & Francis Ltd
Subjects
Bell regression model
Count data
Estimators
Monte Carlo simulation
Multicollinearity
Over-dispersion
Primary: 62J07
Regression models
Ridge estimator
Secondary
Simulation
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Summary:The Poisson Regression Model (PRM) is a well-known model in applications when the response variable consists of count data. However, Bell Regression Model (BRM) is proposed recently as an alternative to the PRM in some cases where the data is over-dispersed. But, multicollinearity between explanatory variables negatively affects traditional estimation methods, such as MLE. Therefore, to avoid this problem, several shrinkage estimators are proposed in the BRM. In this study, a new improved Liu-type estimator is proposed as an alternative to the other proposed biased estimators for the BRM to model count data with over-dispersion. Furthermore, the Monte Carlo simulation studies are executed to compare the performances of the proposed biased estimators. Finally, the obtained results are illustrated in real data.
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content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2023.2252624