In this article, we introduce a new model that extends the inverse Gaussian distribution. This model is obtained when a parameter is incorporated into the logarithmic inverse Gaussian distribution producing great flexibility for fitting non-negative data. We present a comprehensive treatment of the properties of this model, including a derivation of the analytical shapes of the density, distribution, and hazard functions, as well as the moments. Furthermore, we illustrate the use of this model by means of an example using likelihood methods. We show that the new model presents an excellent fit for the analyzed data.
- Birnbaum-Saunders distribution
- Hazard function
- Likelihood methods
- Sinh-normal distribution