Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data
Assessing the multivariate normal approximation of the maximum likelihood estimator from high-dimensional, heterogeneous data
复制标题
根据高维异构数据评估最大似然估计量的多元正态近似
DOI：
10.1214/18-ejs1492
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Andreas Anastasiou
中科院分区：
文献类型：
--
作者：
Andreas Anastasiou
极大似然估计在正则性条件下的渐近正态性是统计理论的基础。本文给出了向量参数的最大似然分布与多元正态分布之间的分布距离的显式上界。我们处理的可能是高维独立的但不一定是相同分布的随机向量。此外，即使在MLE不具有封闭形式表达式的情况下，我们也得到了显式上界。
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 explicit upper bounds even in cases where the MLE does not have a closed-form expression.
影响因子：
1.5
作者：
Anastasiou A
通讯作者：
Anastasiou A