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A semiparametric additive rate model for a modulated renewal process

机译:调制更新过程的半参数加法率模型

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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.
机译:在点流程应用程序中,广泛地遇到了来自长期实现的循环事件数据。对此类数据进行建模和分析不同于对独立且相同的短序列进行建模和分析,并且统计方法的发展需要仔细考虑长单序列的潜在依赖性结构。在本文中,我们提出了一种用于调制更新过程的半参数加法率模型,并为模型参数开发了一种估计方程方法。通过将极限理论应用于固定混合序列,可以建立所得估计量的渐近性质。提出了基于块的自举程序,用于方差估计。进行了仿真研究,以评估所提出估计量的有限样本性能。提供了对心血管死亡率研究的数据集的应用。

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