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

被引：4

作者：

Anastasiou, Andreas ^{[1
]}

机构：

[1] London Sch Econ & Polit Sci, London, England

来源：

STATISTICS & PROBABILITY LETTERS | 2017年 / 129卷

基金：

英国工程与自然科学研究理事会;

关键词：

Maximum likelihood estimator; Dependent random variables; Normal approximation; Stein's method; CENTRAL-LIMIT-THEOREM; ASYMPTOTIC NORMALITY; SUMS;

D O I：

10.1016/j.spl.2017.04.022

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：171 / 181

页数：11

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