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


Ertan E., Algamal Z. Y., Erkoç A., Akay K. U.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1080/03610918.2023.2252624
  • Journal Name: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Istanbul University Affiliated: Yes

Abstract

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.