An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
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DOI：
10.1080/00031305.1992.10475879
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发表时间：
1992-08
期刊：
影响因子：
--
通讯作者：
N. Altman
中科院分区：
文献类型：
--
作者：
N. Altman
摘要非参数回归是一组估计回归曲线的技术，而不对真实回归函数的形状做强有力的假设。因此，这些技术对于建立和检查参数模型以及数据描述是有用的。核和最近邻回归估计量是单变量位置估计量的局部版本，因此它们可以很容易地介绍给熟悉样本均值和中位数等摘要的初学者和咨询客户。
Abstract Nonparametric regression is a set of techniques for estimating a regression curve without making strong assumptions about the shape of the true regression function. These techniques are therefore useful for building and checking parametric models, as well as for data description. Kernel and nearest-neighbor regression estimators are local versions of univariate location estimators, and so they can readily be introduced to beginning students and consulting clients who are familiar with such summaries as the sample mean and median.