An Extension of the Inverse Gaussian Distribution

Abstract

In this study, an a-monotone extension of the inverse Gaussian (aIG) distribution is introduced. Then, the method of moments estimations for the parameters of the aIG distribution is provided. A real dataset is used to show the fitting performance of the aIG distribution. The results show that the aIG distribution fits the corresponding dataset better than the IG distribution if the well-known goodness-of-fit statistics are taken into account. Note that the aIG distribution is defined as a general class of the IG distribution by adding a new shape parameter. It can be considered an alternative to the IG distribution in modeling data from different areas of science. © 2022 by World Scientific Publishing Europe Ltd.