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<p>In this paper a discrete version of the Exponentiated Weibull-Geometric distribution called Discrete Exponentiated Weibull-Geometric(DEWG) distribution is developed and its properties are studied. The parameters are estimated using both classical and Bayesian approaches. The goodness of fit of the newly developed model is evaluated using different simulated datasets and a real dataset. The new model is compared with existing models such as the Poisson, Negative Binomial, and Discrete Weibull distributions using AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and the Kolmogorov–Smirnov test. Furthermore, we develop a novel regression model for count data based on the DEWG distribution, termed the DEWG regression model. The estimation of its parameters is carried out via maximum likelihood (ML) and Bayesian methods. The performance of the newly developed regression model is evaluated using several simulated datasets, and its practical usefulness is demonstrated with a real dataset. The new model is further compared with the existing models using the Vuong test.</p>

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A Comprehensive Regression Model for Count Data based on the Discrete Exponentiated Weibull-Geometric Distribution: Inference and Practical Applications

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Prinsha P. P.,
  • Joby K. Jose,
  • Ramanathan T. V.

摘要

In this paper a discrete version of the Exponentiated Weibull-Geometric distribution called Discrete Exponentiated Weibull-Geometric(DEWG) distribution is developed and its properties are studied. The parameters are estimated using both classical and Bayesian approaches. The goodness of fit of the newly developed model is evaluated using different simulated datasets and a real dataset. The new model is compared with existing models such as the Poisson, Negative Binomial, and Discrete Weibull distributions using AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and the Kolmogorov–Smirnov test. Furthermore, we develop a novel regression model for count data based on the DEWG distribution, termed the DEWG regression model. The estimation of its parameters is carried out via maximum likelihood (ML) and Bayesian methods. The performance of the newly developed regression model is evaluated using several simulated datasets, and its practical usefulness is demonstrated with a real dataset. The new model is further compared with the existing models using the Vuong test.