Monte Carlo comparison of goodness-of-fit tests for the Inverse Gaussian distribution based on empirical distribution function

Abstract

The Inverse Gaussian (IG) distribution is widely used to model positively skewed data. In this article, we examine goodness of fit tests for the Inverse Gaussian distribution based on the empirical distribution function. In order to compute the test statistics, parameters of the Inverse Gaussian distribution are estimated by maximum likelihood estimators (MLEs), which are simple explicit estimators. Critical points and the actual sizes of the tests are obtained by Monte Carlo simulation. Through a simulation study, power values of the tests are compared with each other. Finally, an illustrative example is presented and analyzed.

Keywords

• empirical distribution function
• inverse gaussian distribution
• maximum likelihood estimates
• goodness-of-fit test