Kernel-Type Estimators of Jump Points and Values of a Regression Function
Kernel-Type Estimators of Jump Points and Values of a Regression Function
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DOI：
10.1214/aos/1176349271
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发表时间：
1993-09
影响因子：
4.5
通讯作者：
J. Wu;C. Chu
中科院分区：
文献类型：
--
作者：
J. Wu;C. Chu
在固定设计非参数回归模型中，给出了回归函数跳点位置和跳值大小的核估计。这些核型估计分析几乎肯定的结果和极限分布。利用极限分布，我们能够检验跳跃点的数目，并给出回归函数跳跃值大小的渐近置信区间。模拟研究表明，渐近结果保持合理的样本容量。
In the fixed-design nonparametric regression model, kernel-type estimators of the locations of jump points and the corresponding sizes of jump values of the regression function are proposed. These kernel-type estimators are analyzed with almost sure results and limiting distributions. Using the limiting distributions, we are able to test the number of jump points and give asymptotic confidence intervals for the sizes of jump values of the regression function. Simulation studies demonstrate that the asymptotic results hold for reasonable sample sizes.