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Dynamic factor analysis of high-dimensional recurrent events

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  • 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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    References listed on IDEAS

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    1. Y Chen & X Li, 2022. "Determining the number of factors in high-dimensional generalized latent factor models [Eigenvalue ratio test for the number of factors]," Biometrika, Biometrika Trust, vol. 109(3), pages 769-782.
    2. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    5. Wei Liu & Huazhen Lin & Shurong Zheng & Jin Liu, 2023. "Generalized Factor Model for Ultra-High Dimensional Correlated Variables with Mixed Types," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1385-1401, April.
    6. Bai, Jushan & Ng, Serena, 2023. "Approximate factor models with weaker loadings," Journal of Econometrics, Elsevier, vol. 235(2), pages 1893-1916.
    7. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    8. Chen, Yunxiao & Li, Xiaoou, 2022. "Determining the number of factors in high-dimensional generalized latent factor models," LSE Research Online Documents on Economics 111574, London School of Economics and Political Science, LSE Library.
    9. Yunxiao Chen, 2020. "A Continuous-Time Dynamic Choice Measurement Model for Problem-Solving Process Data," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 1052-1075, December.
    10. Lu Yang, 2022. "Nonparametric Copula Estimation for Mixed Insurance Claim Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 537-546, April.
    11. Niansheng Tang & Sy-Miin Chow & Joseph G. Ibrahim & Hongtu Zhu, 2017. "Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 875-903, December.
    12. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-1304, September.
    13. He, Yong & Kong, Xinbing & Trapani, Lorenzo & Yu, Long, 2023. "One-way or two-way factor model for matrix sequences?," Journal of Econometrics, Elsevier, vol. 235(2), pages 1981-2004.
    14. Michel Wedel & Wagner Kamakura, 2001. "Factor analysis with (mixed) observed and latent variables in the exponential family," Psychometrika, Springer;The Psychometric Society, vol. 66(4), pages 515-530, December.
    15. Wang, Dong & Liu, Xialu & Chen, Rong, 2019. "Factor models for matrix-valued high-dimensional time series," Journal of Econometrics, Elsevier, vol. 208(1), pages 231-248.
    16. Chen, Yunxiao & Zhang, Siliang, 2020. "A latent Gaussian process model for analysing intensive longitudinal data," LSE Research Online Documents on Economics 101121, London School of Economics and Political Science, LSE Library.
    17. Zhiming Xia & Peihua Qiu, 2015. "Jump information criterion for statistical inference in estimating discontinuous curves," Biometrika, Biometrika Trust, vol. 102(2), pages 397-408.
    18. Yunxiao Chen & Xiaoou Li & Siliang Zhang, 2020. "Structured Latent Factor Analysis for Large-scale Data: Identifiability, Estimability, and Their Implications," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1756-1770, December.
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    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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