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：我们构建了单调回归函数的bootstrap置信区间。已有研究表明，基于非参数最小二乘估计量(LSE) $\hat f_n$ 的普通非参数bootstrap在此情形下是不一致的。然而，我们证明基于平滑后的$\hat f_n$（称为SLSE，即平滑最小二乘估计量）可以构建一致的bootstrap方法。本文推导了SLSE的渐近逐点分布。基于平滑bootstrap的置信区间与基于（未必单调的）Nadaraya-Watson估计量的区间进行了比较，并研究了学生化效应的影响。此外，我们提出了一种自动带宽选择方法，修正了Sen与Xu（2015）的工作。该过程通过一个与气候变化相关的公开数据集进行了展示。