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Dynamic factor analysis of high-dimensional recurrent events

Author

Listed:
  • Chen, Fangyi
  • Chen, Yunxiao
  • Ying, Zhiliang
  • Zhou, Kangjie

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.

Suggested Citation

  • Chen, Fangyi & Chen, Yunxiao & Ying, Zhiliang & Zhou, Kangjie, 2025. "Dynamic factor analysis of high-dimensional recurrent events," LSE Research Online Documents on Economics 127778, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:127778
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    File URL: http://eprints.lse.ac.uk/127778/
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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