This article focuses on the problem of kernel regression estimation in the presence of nonignorable incomplete data with particular focus on the limiting distribution of the maximal deviation of the proposed estimators. From an applied point of view, such a limiting distribution enables one to construct asymptotically correct uniform bands, or perform tests of hypotheses, for a regression curve when the available data set suffers from missing (not necessarily at random) response values. Furthermore, such asymptotic results have always been of theoretical interest in mathematical statistics. We also present some numerical results that further confirm and complement the theoretical developments of this paper.

本文重点研究存在不可忽略的不完整数据时的核回归估计问题，特别关注所提出的估计量的最大偏差的限制分布。从应用的角度来看，当可用数据集缺少（不一定是随机的）响应值时，这种限制分布使人们能够为回归曲线构建渐近正确的均匀带，或执行假设检验。此外，这种渐近结果在数理统计中一直具有理论意义。我们还提出了一些数值结果，进一步证实和补充了本文的理论发展。