Bounds for the multivariate normal approximation of the maximum likelihood estimator
Bounds for the multivariate normal approximation of the maximum likelihood estimator
复制标题
最大似然估计量的多元正态近似的界限
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Andreas Anastasiou
中科院分区：
文献类型：
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作者：
Andreas Anastasiou
正则性条件下极大似然估计(MLE)的渐近正态是统计理论的基石。本文给出了可能高维参数的极大似然估计的分布与多元正态分布之间的分布距离的显式上界。不需要最大似然估计的显式解析表达式，随机向量是独立的，但不一定是同分布的。
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 possibly-high dimensional parameter, and the multivariate normal. An explicit analytical expression of the MLE is not required and the random vectors are independent but not necessarily identically distributed.