We construct bootstrap confidence intervals for a monotone regression function. It has been shown that the ordinary nonparametric bootstrap, based on the nonparametric least squares estimator (LSE) $\hat f_n$ is inconsistent in this situation. We show, however, that a consistent bootstrap can be based on the smoothed $\hat f_n$, to be called the SLSE (Smoothed Least Squares Estimator). The asymptotic pointwise distribution of the SLSE is derived. The confidence intervals, based on the smoothed bootstrap, are compared to intervals based on the (not necessarily monotone) Nadaraya Watson estimator and the effect of Studentization is investigated. We also give a method for automatic bandwidth choice, correcting work in Sen and Xu (2015). The procedure is illustrated using a well known dataset related to climate change.

翻译：我们为单调回归函数构建了自助法置信区间。已有研究表明，基于非参数最小二乘估计量 $\hat f_n$ 的普通非参数自助法在此情形下不具有一致性。然而，我们证明基于平滑后的 $\hat f_n$（称为平滑最小二乘估计量，SLSE）的自助法具有一致性。我们推导了SLSE的渐近逐点分布。将基于平滑自助法的置信区间与基于（未必单调的）Nadaraya-Watson估计量的区间进行比较，并研究了学生化变换的影响。我们还提出了一种自动带宽选择方法，对Sen和Xu（2015）的研究进行了修正。该过程通过一个与气候变化相关的著名数据集进行了示例说明。