Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference
Method G: Uncertainty Quantification for Distributed Data Problems Using Generalized Fiducial Inference
复制标题
方法 G：使用广义基准推理对分布式数据问题进行不确定性量化
DOI：
10.1080/10618600.2021.1923514
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发表时间：
2021
影响因子：
2.4
通讯作者：
Lee, Thomas C.
中科院分区：
文献类型：
--
作者：
Lai, Randy C.;Hannig, Jan;Lee, Thomas C.
数据分析师遇到分布在多台计算机上的数据集并不罕见。这可能是由于隐私问题、可能性评估的效率或整个数据集的大小等原因。这给统计人员带来了新的挑战，因为即使是计算简单的汇总统计数据，如中位数，也变得具有计算挑战性。此外，如果需要其他先进的统计方法，则需要新的计算策略。在这篇文章中，我们提出了一种新的分布式分析的海量数据，适用于广义置信推理，是基于一个认真实施的“分而治之”的战略结合重要性抽样。所提出的方法只需要少量的节点之间的通信，并被证明是渐近等价于使用整个数据集。与大多数现有的方法不同，所提出的方法产生的不确定性措施（如置信区间），除了点估计参数的兴趣。所提出的方法也适用于一个大的太阳图像集的分析。本文的补充材料可在网上查阅。
It is not unusual for a data analyst to encounter datasets distributed across several computers. This can happen for reasons such as privacy concerns, efficiency of likelihood evaluations, or just the sheer size of the whole dataset. This presents new challenges to statisticians as even computing simple summary statistics such as the median becomes computationally challenging. Furthermore, if other advanced statistical methods are desired, then novel computational strategies are needed. In this article, we propose a new approach for distributed analysis of massive data that is suitable for generalized fiducial inference and is based on a careful implementation of a “divide-and-conquer” strategy combined with importance sampling. The proposed approach requires only small amount of communication between nodes, and is shown to be asymptotically equivalent to using the whole dataset. Unlike most existing methods, the proposed approach produces uncertainty measures (such as confidence intervals) in addition to point estimates for parameters of interest. The proposed approach is also applied to the analysis of a large set of solar images. Supplementary materials for this article are available online.
影响因子：
6.1
作者：
GEWEKE, J
通讯作者：
GEWEKE, J
用于实验室间比较的融合学习
影响因子：
0.9
作者：
Hannig, Jan;Feng, Qing;Iyer, Hari;Wang, C.M.;Liu, Xuhua
通讯作者：
Liu, Xuhua