A unified mixture model based on the inverse gaussian distribution

In this paper, we introduce a new class of mixture models based on the inverse Gaussian distribution, which is highly flexible and contains several well-known probability models. The new class of models is generated from symmetric distributions around zero by using the connection between the inverse Gaussian and standard normal distributions. We illustrate the obtained results by means of two real data sets through likelihood, goodness-of-fit and diagnostic methods. This illustration indicates the adequacy of the new model.