Online two‐way estimation and inference via linear mixed‐effects models
Online two‐way estimation and inference via linear mixed‐effects models
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DOI：
10.1002/sim.9557
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发表时间：
2022-08
影响因子：
2
通讯作者：
Lan Luo;Lexin Li
中科院分区：
文献类型：
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作者：
Lan Luo;Lexin Li
在本文中，我们解决了分析在多个数据站点上连续收集的分布式流数据的估计和推理问题。我们提出了一种通过线性混合效应模型的在线双向方法。我们明确地将特定站点的效应建模为随机效应项，并处理站点之间的异质性和站点内的相关性。我们开发了一种在线更新程序，该程序不需要重新访问以前的数据，并且可以在新的数据站点或现有数据站点的新样本观测流可用时有效地更新参数估计。我们导出了我们所提出的在线估计量的非渐近误差界，并证明了它与基于所有原始数据的离线估计量是渐近等价的。我们对一些关键的替代方案进行了分析和数值比较，并证明了我们的建议的优势。我们将用两个数据应用程序进一步说明我们的方法。
In this article, we tackle the estimation and inference problem of analyzing distributed streaming data that is collected continuously over multiple data sites. We propose an online two‐way approach via linear mixed‐effects models. We explicitly model the site‐specific effects as random‐effect terms, and tackle both between‐site heterogeneity and within‐site correlation. We develop an online updating procedure that does not need to re‐access the previous data and can efficiently update the parameter estimate, when either new data sites, or new streams of sample observations of the existing data sites, become available. We derive the non‐asymptotic error bound for our proposed online estimator, and show that it is asymptotically equivalent to the offline counterpart based on all the raw data. We compare with some key alternative solutions both analytically and numerically, and demonstrate the advantages of our proposal. We further illustrate our method with two data applications.