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Area of Science:

  • Statistics
  • Econometrics
  • Data Science

Background:

  • Count data analysis is crucial in various fields.
  • Existing regression models like Negative Binomial have limitations.
  • The generalized Waring distribution offers a flexible alternative for count data.

Purpose of the Study:

  • To introduce and evaluate a novel regression model based on the generalized Waring distribution.
  • To develop diagnostic techniques for assessing model influence.
  • To compare the performance of the generalized Waring regression model against existing models.

Main Methods:

  • Development of local influence diagnostic techniques using likelihood displacement.
  • Implementation of case-deletion methods for influence analysis.
  • Estimation via maximum likelihood function.
  • Comparison with Negative Binomial and Waring regression models.

Main Results:

  • The generalized Waring regression model demonstrates superior performance compared to Negative Binomial and Waring models.
  • Influence measures were successfully applied to real-world data.
  • The model effectively captures complex count data patterns.

Conclusions:

  • The generalized Waring regression model is a robust and effective tool for count data analysis.
  • The developed diagnostic methods aid in reliable model assessment.
  • This approach offers significant advantages over traditional count data regression models.