高维复发事件数据的动态因子分析
Dynamic Factor Analysis of High-dimensional Recurrent Events
AI总结：
本文提出一种半参数动态因子模型，用于高维复发事件数据降维，通过施加低维结构并允许依赖关系，提出近最优估计量与因子选择准则，模拟与购物数据应用验证了其有效性。
摘要：
复发事件时间数据出现在许多研究中，包括生物医学、公共卫生、营销和社交媒体分析。随着信息技术的发展，涉及多种事件类型和观测的高维复发事件数据变得普遍。本文提出了一种用于高维复发事件数据降维的半参数动态因子模型。该模型对事件类型的均值强度函数施加低维结构，同时允许存在依赖关系。提出了一种基于平滑的近最优速率估计量，并开发了一种能一致选择因子个数的信息准则。模拟研究证明了这些推断工具的有效性。该方法被应用于杂货购物数据，获得了可解释的因子结构。
英文摘要：
Recurrent event time data arise in many studies, including biomedicine, public health, marketing, and social media analysis. High-dimensional recurrent event data involving many event types and observations have become prevalent with advances in information technology. This paper proposes a semiparametric dynamic factor model for the dimension reduction of high-dimensional recurrent event data. The proposed model imposes a low-dimensional structure on the mean intensity functions of the event types while allowing for dependencies. A nearly rate-optimal smoothing-based estimator is proposed. An information criterion that consistently selects the number of factors is also developed. Simulation studies demonstrate the effectiveness of these inference tools. The proposed method is applied to grocery shopping data, for which an interpretable factor structure is obtained.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
