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Two New Mixture Models Related to the Inverse Gaussian Distribution
Authors:Samuel Kotz  Víctor Leiva  Antonio Sanhueza
Affiliation:1.Department of Engineering Management and Systems Engineering,The George Washington University,Washington,USA;2.Departamento de Estadística,Universidad de Valparaíso,Valparaíso,Chile;3.Departamento de Matematica y Estadística,Universidad de La Frontera,Temuco,Chile
Abstract:This article presents a new family of logarithmic distributions to be called the sinh mixture inverse Gaussian model and its associated life distribution referred as the extended mixture inverse Gaussian model. Specifically, the density, distribution function, and moments are developed for the sinh mixture inverse Gaussian distribution. Next, the extended mixture inverse Gaussian distribution is characterized. A graphical analysis of the densities of the new models is also provided. In addition, a lifetime analysis is presented for the extended mixture inverse Gaussian distribution. Finally, an example with a real data set is given to illustrate the methodology, which indicates that the new models result in a better fit to the data than some other well-known distributions.
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