10.1007/s40096-019-0293-5

A new model for over-dispersed count data: Poisson quasi-Lindley regression model

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  • Emrah Altun*1,
  1. Department of Mathematics, Bartin University, Bartin, 74100, TR
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Published in Issue 2019-07-10

How to Cite

Altun, E. (2019). A new model for over-dispersed count data: Poisson quasi-Lindley regression model. Mathematical Sciences, 13(3 (September 2019). https://doi.org/10.1007/s40096-019-0293-5
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Abstract

Abstract In this paper, a new regression model for count response variable is proposed via re-parametrization of Poisson quasi-Lindley distribution. The maximum likelihood and method of moment estimations are considered to estimate the unknown parameters of re-parametrized Poisson quasi-Lindley distribution. The simulation study is conducted to evaluate the efficiency of estimation methods. The real data set is analyzed to demonstrate the usefulness of proposed model against the well-known regression models for count data modeling such as Poisson and negative-binomial regression models. Empirical results show that when the response variable is over-dispersed, the proposed model provides better results than other competitive models.

Keywords

  • Count data,
  • Poisson regression,
  • Negative-binomial regression,
  • Maximum Likelihood,
  • Method of moments,
  • Over-dispersion
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