Dynamic factor analysis of high-dimensional recurrent events

Fangyi Chen, Yunxiao Chen, Zhiliang Ying and Kangjie Zhou

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Summary: Recurrent event time data arise in many studies, including in 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 article 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.

Keywords: counting process; factor analysis; marginal modelling; kernal smoothing; information criterion (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2025-07-21
New Economics Papers: this item is included in nep-ecm and nep-ets
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Published in Biometrika, 21, July, 2025, 112(3). ISSN: 0006-3444

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:127778

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