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

Andreas Anastasiou

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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; estimatorDependent; random; variablesNormal; approximationStein’s; method (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Date: 2017-06-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (3)

Published in Statistics and Probability Letters, 9, June, 2017, 129, pp. 171-181. ISSN: 0167-7152

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