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The mixed Poisson–inverse‐Gaussian distribution has been used by Holla, Sankaran, Sichel, and others in univariate problems involving counts. We propose a Poisson–inverse‐Gaussian regression model which can be used for regression analysis of counts. The model provides an attractive framework for incorporating random effects in Poisson regression models and in handling extra‐Poisson variation. Maximum‐likelihood and quasilikelihood‐moment estimation is investigated and illustrated with an example involving motor‐insurance claims.