Testing For Global Covariate Effects in Dynamic Interaction Event Networks

IF 2.9 2区 数学 Q1 ECONOMICS Journal of Business & Economic Statistics Pub Date : 2023-09-26 DOI:10.1080/07350015.2023.2263537
Alexander Kreiss, Enno Mammen, Wolfgang Polonik
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Abstract

AbstractIn statistical network analysis it is common to observe so called interaction data. Such data is characterized by actors forming the vertices and interacting along edges of the network, where edges are randomly formed and dissolved over the observation horizon. In addition covariates are observed and the goal is to model the impact of the covariates on the interactions. We distinguish two types of covariates: global, system-wide covariates (i.e. covariates taking the same value for all individuals, such as seasonality) and local, dyadic covariates modeling interactions between two individuals in the network. Existing continuous time network models are extended to allow for comparing a completely parametric model and a model that is parametric only in the local covariates but has a global non-parametric time component. This allows, for instance, to test whether global time dynamics can be explained by simple global covariates like weather, seasonality etc. The procedure is applied to a bike-sharing network by using weather and weekdays as global covariates and distances between the bike stations as local covariates.Keywords: Dynamic NetworksCounting ProcessesDependenceDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also.
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动态交互事件网络中全局协变量效应的检验
摘要在统计网络分析中,通常会观察到所谓的交互数据。这些数据的特点是参与者形成顶点,并沿着网络的边缘进行交互,其中边缘是随机形成的,并在观测视界上溶解。此外,还观察了协变量,目的是对协变量对相互作用的影响进行建模。我们区分了两种类型的协变量:全局的、系统范围的协变量(即对所有个体取相同值的协变量,如季节性)和局部的、二元的协变量,这些协变量模拟了网络中两个个体之间的相互作用。现有的连续时间网络模型进行了扩展,以允许比较完全参数模型和仅在局部协变量中参数化但具有全局非参数时间分量的模型。例如,这允许测试全球时间动态是否可以用天气、季节性等简单的全球协变量来解释。以天气和工作日为全局协变量,以自行车站点之间的距离为局部协变量,将该方法应用于共享单车网络。关键词:动态网络计数过程依赖性免责声明作为对作者和研究人员的服务,我们提供此版本的已接受手稿(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。
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来源期刊
Journal of Business & Economic Statistics
Journal of Business & Economic Statistics 数学-统计学与概率论
CiteScore
5.00
自引率
6.70%
发文量
98
审稿时长
>12 weeks
期刊介绍: The Journal of Business and Economic Statistics (JBES) publishes a range of articles, primarily applied statistical analyses of microeconomic, macroeconomic, forecasting, business, and finance related topics. More general papers in statistics, econometrics, computation, simulation, or graphics are also appropriate if they are immediately applicable to the journal''s general topics of interest. Articles published in JBES contain significant results, high-quality methodological content, excellent exposition, and usually include a substantive empirical application.
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