Recurrent event data with an additive marginal rates function have been extensively studied in the literature. The existing statistical inference, however, faces the difficulty with high-dimensional covariates due to “curse of dimensionality”. Examples include gene expression and single nucleotide polymorphism data which have revolutionized our understanding of disease such as cancer recurrence. In this paper, a technique of partial sufficient dimension reduction is applied to an additive rates model for recurrent event data. A two-step procedure is proposed to estimate parameters. First, partial sufficient dimension reduction is used to estimate the basis of the partial central subspace and the structural dimension. Then the second step estimates the baseline and the regression function of covariates based on the estimated partial central subspace using the average surface method. Simulation is performed to confirm and assess the theoretical findings, and an application on a set of chronic granulomatous disease data is demonstrated.

中文翻译：

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具有高维协变量的复发事件数据的加性率模型的部分充分降维

文献中已经广泛研究了具有附加边际率函数的复发事件数据。然而，由于“维数灾难”，现有的统计推断面临着高维协变量的困难。例子包括基因表达和单核苷酸多态性数据，它们彻底改变了我们对癌症复发等疾病的理解。在本文中，将部分充分降维技术应用于重复事件数据的加性率模型。提出了一个两步程序来估计参数。首先，使用部分充分降维来估计部分中心子空间和结构维数的基础。然后第二步基于估计的部分中心子空间使用平均表面方法估计基线和协变量的回归函数。执行模拟以确认和评估理论发现，并演示了在一组慢性肉芽肿病数据上的应用。