Comparing Between Maximum Likelihood and Bayesian method for Estimating Poisson Regression Model with Application to Lung Cancer Data in Erbil-Kurdistan/Iraq
DOI:
https://doi.org/10.31272/jae.i150.1445Keywords:
Poisson Regression, Bayesian Method, Maximum Likelihood , Markov Chain Monte Carlo , Lung CancerAbstract
The basic idea of this study focused on using the Bayesian method and the maximum likelihood estimation in Poisson regression to model the incidence of lung cancer in Erbil City, Iraq. Poisson regression is commonly used to analyze count data, which makes it suitable to apply it in analyzing disease rates in medical data. The study compares the performance of maximum likelihood with the Bayesian method, which incorporates the prior distribution in count for parameter estimation. The data set of this study, which includes lung cancer cases with possible risk factors, was obtained from Rizgary Hospital in Erbil City.
The Bayesian estimation method uses the Markov Chain Monte Carlo approach to create posterior distribution, and the effectiveness of both methods is diagnosed and goodness of fit is tested, and model performance.
The results indicated that both methods effectively identified significant factors of lung cancer and have reached the same factors that have an impact on lung cancer data in Erbil city, and Bayesian methods give better performance and provide more interpretable quantifications. The results were obtained by using the statistical packages (SPSS v.26, Stata V. 18, and R V. 4.3.1).
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