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Bounds for the normal approximation of the maximum likelihood estimator

Research paper by Andreas Anastasiou, Gesine Reinert

Indexed on: 24 Apr '15Published on: 24 Apr '15Published in: Mathematics - Statistics



Abstract

While the asymptotic normality of the maximum likelihood estimator under regularity conditions is long established, in this paper we derive explicit bounds for the bounded Wasserstein distance between the distribution of the maximum likelihood estimator (MLE) and the normal distribution. For this task we employ Stein's method. We focus on independent and identically distributed random variables, covering both discrete and continuous distributions as well as exponential and non-exponential families. In particular, we do not require a closed form expression of the MLE. We also use a perturbation method to treat cases where the MLE has positive probability of being on the boundary of the parameter space.