Outliers Data Mining in Normal-Inverse Gaussian Model

The normal-inverse model arises as a normal variance-mean mixture with an inverse Gaussian mixing model. The resulting model, it is very complicated to obtain the influence measures based on the tradition method. In the present paper, several diagnostic measures for outlier data mining are obtained based on the conditional expectation of the complete-data log-likelihood function based on the EM algorithm. An example for which we apply the diagnosis methods is given as illustration.