Quantile regression has emerged as an important analytical alternative to the classical mean regression model. However, the analysis could be complicated by the presence of censored measurements due to a detection limit of equipment in combination with unavoidable missing values arising when, for instance, a researcher is simply unable to collect an observation. Another complication arises when measures depart significantly from normality, for instance, in the presence of skew heavy-tailed observations. For such data structures, we propose a robust quantile regression for censored and/or missing responses based on the skew-t distribution. A computationally feasible EM-based procedure is developed to carry out the maximum likelihood estimation within such a general framework. Moreover, the asymptotic standard errors of the model parameters are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and two real data sets.

中文翻译：

---

删失数据和缺失数据的 skew-t 分位数回归

分位数回归已成为经典平均回归模型的重要分析替代方法。然而，由于设备的检测极限以及不可避免的缺失值（例如，研究人员无法收集观察结果）而存在截尾测量，分析可能会变得复杂。当测量值明显偏离正态时，会出现另一种复杂情况，例如，存在倾斜的重尾观测。对于这样的数据结构，我们提出了基于 skew- t 的针对删失和/或缺失响应的稳健分位数回归分配。开发了一种计算上可行的基于 EM 的程序，以在这种一般框架内执行最大似然估计。此外，模型参数的渐近标准误差是通过基于信息的方法明确获得的。我们通过使用模拟数据和两个真实数据集来说明我们的方法。