Abstract This paper proposes a method to conduct local linear regression smoothing in the presence of set-valued outcome data. The proposed estimator is shown to be consistent, and its mean squared error and asymptotic distribution are derived. A method to build error tubes around the estimator is provided, and a small Monte Carlo exercise is conducted to confirm the good finite sample properties of the estimator. The usefulness of the method is illustrated on a novel dataset from a clinical trial to assess the effect of certain genes' expressions on different lung cancer treatments outcomes.

中文翻译：

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具有设定值结果数据的局部回归平滑器

摘要 本文提出了一种在存在设置值结果数据的情况下进行局部线性回归平滑的方法。所提出的估计量被证明是一致的，并推导出其均方误差和渐近分布。提供了一种在估计器周围构建误差管的方法，并进行了小型蒙特卡罗练习以确认估计器良好的有限样本特性。该方法的有用性在来自临床试验的新数据集上得到了说明，以评估某些基因表达对不同肺癌治疗结果的影响。