The asymptotic normality of the Maximum Likelihood Estimator (MLE) is a long established result. Explicit bounds for the distributional distance between the distribution of the MLE and the normal distribution have recently been obtained for the case of independent random variables. In this paper, a local dependence structure is introduced between the random variables and we give upper bounds which are specified for the Wasserstein metric.
Bounds for the normal approximation of the maximum likelihood estimator
Bounds for the multivariate normal approximation of the maximum likelihood estimator
Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
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