Current status data arise frequently in demography, epidemiology, and econometrics where the exact failure time cannot be determined but is only known to have occurred before or after a known observation time. We propose a quantile regression model to analyze current status data, because it does not require distributional assumptions and the coefficients can be interpreted as direct regression effects on the distribution of failure time in the original time scale. Our model assumes that the conditional quantile of failure time is a linear function of covariates. We assume conditional independence between the failure time and observation time. An M-estimator is developed for parameter estimation which is computed using the concave-convex procedure and its confidence intervals are constructed using a subsampling method. Asymptotic properties for the estimator are derived and proven using modern empirical process theory. The small sample performance of the proposed method is demonstrated via simulation studies. Finally, we apply the proposed method to analyze data from the Mayo Clinic Study of Aging.

中文翻译：

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当前状态数据的分位数回归模型


当前状态数据经常出现在人口学、流行病学和计量经济学中，其中确切的故障时间无法确定，只能知道发生在已知观察时间之前或之后。我们提出了一种分位数回归模型来分析当前状态数据，因为它不需要分布假设，并且系数可以解释为对原始时间尺度内故障时间分布的直接回归效应。我们的模型假设故障时间的条件分位数是协变量的线性函数。我们假设故障时间和观察时间之间条件独立。开发了用于参数估计的 M 估计器，该估计器使用凹凸过程进行计算，并使用子采样方法构建其置信区间。使用现代经验过程理论导出并证明了估计量的渐近属性。通过模拟研究证明了所提出方法的小样本性能。最后，我们应用所提出的方法来分析梅奥诊所衰老研究的数据。