Bounds for the normal approximation of the maximum likelihood estimator from m-dependent random variables

Andreas Anastasiou

Statistics & Probability Letters, 2017, vol. 129, issue C, 171-181

Abstract: 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.

Keywords: Maximum likelihood estimator; Dependent random variables; Normal approximation; Stein’s method (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.spl.2017.04.022

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