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A semiparametric additive rate model for a modulated renewal process

Author

Listed:
  • Xin Chen

    (Chinese Academy of Sciences)

  • Jieli Ding

    (Wuhan University)

  • Liuquan Sun

    (Chinese Academy of Sciences)

Abstract

Recurrent event data from a long single realization are widely encountered in point process applications. Modeling and analyzing such data are different from those for independent and identical short sequences, and the development of statistical methods requires careful consideration of the underlying dependence structure of the long single sequence. In this paper, we propose a semiparametric additive rate model for a modulated renewal process, and develop an estimating equation approach for the model parameters. The asymptotic properties of the resulting estimators are established by applying the limit theory for stationary mixing sequences. A block-based bootstrap procedure is presented for the variance estimation. Simulation studies are conducted to assess the finite-sample performance of the proposed estimators. An application to a data set from a cardiovascular mortality study is provided.

Suggested Citation

  • Xin Chen & Jieli Ding & Liuquan Sun, 2018. "A semiparametric additive rate model for a modulated renewal process," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 675-698, October.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:4:d:10.1007_s10985-017-9413-4
    DOI: 10.1007/s10985-017-9413-4
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    References listed on IDEAS

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