Some approximations for the asymptotic variance of the maximum likelihood estimator of the parameter in the inverse Hypergeometric random variable

We derive some approximations for the asymptotic variance of the Maximum Likelihood estimator for the parameter of the Inverse Hypergeometric random variable. For most statistical models, the asymptotic variance is usually derived after some algebraic manipulations. In this paper, we show that this lengthy calculations can be overcome by simple and accurate linear approximations. The interest for this result arises from a statistical model for preferences that has been recently proposed for evaluation studies, preferences analyses and marketing researches.