标题： 带删失数据的高破裂点稳健回归 显示英文标题

标题： High breakdown point robust regression with censored data

摘要： 本文提出了一类线性回归模型的高破裂点估计量，其中响应变量包含删失观察值。 这些估计量对高杠杆离群值具有鲁棒性，并推广了线性回归中的LMS（最小平方中位数）、S、MM和$\tau$-估计量。 本文的一个重要贡献是，我们可以使用有界损失函数（或等价地，递减评分函数）定义一致估计量。 由于计算这些估计量可能计算成本高昂，我们提出了一个有效的算法来计算它们。 我们通过一个例子说明了它们的使用，并进行了模拟研究，结果显示这些估计量也具有良好的有限样本性质。 显示英文摘要

摘要： In this paper, we propose a class of high breakdown point estimators for the linear regression model when the response variable contains censored observations. These estimators are robust against high-leverage outliers and they generalize the LMS (least median of squares), S, MM and $\tau$-estimators for linear regression. An important contribution of this paper is that we can define consistent estimators using a bounded loss function (or equivalently, a redescending score function). Since the calculation of these estimators can be computationally costly, we propose an efficient algorithm to compute them. We illustrate their use on an example and present simulation studies that show that these estimators also have good finite sample properties.