Prediction Limits for the Inverse Gaussian Distribution

Abstract

Prediction intervals for the inverse Gaussian are obtained from both a frequentist and a Bayesian viewpoint. The frequentist intervals are obtained by constructing pivotals that have the x 2 and F distributions. The method involves inversion of probability statements, which results in two-sided prediction intervals. A Bayesian predictive density is obtained using a vague prior, from which one- or two-sided Bayesian prediction intervals can be determined. An example for which Bayesian prediction limits are narrower than the frequentist is given.