Almost Unbiased Liu Type Estimator in Bell Regression Model: Theory, Simulation and Application

2025/05/27 by Caner Tanış, Tanış, Caner, Yasin Asar +1
Medicine · #FOS: Computer and information sciences #FOS: Mathematics #Mathematical and Theoretical Epidemiology and Ecology Models #Methodology (stat.ME) #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2505.20946

openalex publication_date 2025/05/27 · openalex created_date 2025/10/16 · openalex updated_date 2026/07/28

Abstract

In this paper, we gain the new almost unbiased Liu-type estimators to literature for the Bell regression model. We provide the superiority of the proposed estimator to its competitors such as the maximum likelihood estimator and Liu-type estimators via some theorems. We also design an extensive Monte Carlo simulation study to show that the proposed estimators outperforms the competitors in terms of mean squared error theoretically. Finally, we present a real data study to assess the performance of the introduced estimators in modeling real-life data. The findings of both the simulation and the empirical study demonstrate that the proposed regression estimators surpasses its competitors based on the mean square error criterion.

Citations

• Liu-type Negative Binomial Regression: A Comparison of Recent Estimators and Applications
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems

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