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2018 Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data
Andreas Anastasiou
Electron. J. Statist. 12(2): 3794-3828 (2018). DOI: 10.1214/18-EJS1492

Abstract

The asymptotic normality of the maximum likelihood estimator (MLE) under regularity conditions is a cornerstone of statistical theory. In this paper, we give explicit upper bounds on the distributional distance between the distribution of the MLE of a vector parameter, and the multivariate normal distribution. We work with possibly high-dimensional, independent but not necessarily identically distributed random vectors. In addition, we obtain upper bounds in cases where the MLE cannot be expressed analytically.

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Andreas Anastasiou. "Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data." Electron. J. Statist. 12 (2) 3794 - 3828, 2018. https://doi.org/10.1214/18-EJS1492

Information

Received: 1 August 2017; Published: 2018
First available in Project Euclid: 30 November 2018

zbMATH: 06987203
MathSciNet: MR3881763
Digital Object Identifier: 10.1214/18-EJS1492

Subjects:
Primary: 62F12
Secondary: 62E17

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Vol.12 • No. 2 • 2018
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