Relational Event Modeling

IF 7.4 1区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Annual Review of Statistics and Its Application Pub Date : 2023-11-28 DOI:10.1146/annurev-statistics-040722-060248

Federica Bianchi, Edoardo Filippi-Mazzola, Alessandro Lomi, Ernst C. Wit

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引用次数: 0

Abstract

Advances in information technology have increased the availability of time-stamped relational data, such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the events frequently contains information that requires new models for the analysis of continuous-time interactions, subject to both endogenous and exogenous influences. The introduction of the relational event model (REM) has been a major development that has stimulated new questions and led to further methodological developments. In this review, we track the intellectual history of the REM, define its core properties, and discuss why and how it has been considered useful in empirical research. We describe how the demands of novel applications have stimulated methodological, computational, and inferential advancements.Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 11 is March 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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关系事件建模

信息技术的进步增加了时间戳关系数据的可用性，例如通过电子邮件交换或通过社交媒体互动产生的数据。虽然相关的信息流可以汇总成横截面面板，但事件的时间顺序往往包含需要新的模型来分析受内生和外生影响的连续时间相互作用的信息。关系事件模型(REM)的引入是一项重大发展，它激发了新的问题，并导致了进一步的方法论发展。在这篇综述中，我们追溯了快速眼动的思想史，定义了它的核心属性，并讨论了它为什么以及如何在实证研究中被认为是有用的。我们描述了新应用的需求如何刺激了方法、计算和推理的进步。预计《统计年鉴及其应用》第11卷的最终在线出版日期为2024年3月。修订后的估计数请参阅http://www.annualreviews.org/page/journal/pubdates。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Annual Review of Statistics and Its Application MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY

CiteScore

13.40

自引率

1.30%

发文量

期刊介绍： The Annual Review of Statistics and Its Application publishes comprehensive review articles focusing on methodological advancements in statistics and the utilization of computational tools facilitating these advancements. It is abstracted and indexed in Scopus, Science Citation Index Expanded, and Inspec.