An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression
作者:
N.S. Altman,
期刊:
The American Statistician
(Taylor Available online 1992)
卷期:
Volume 46,
issue 3
页码: 175-185
ISSN:0003-1305
年代: 1992
DOI:10.1080/00031305.1992.10475879
出版商: Taylor & Francis Group
关键词: Confidence intervals;Local linear regression;Model building;Model checking;Smoothing
数据来源: Taylor
摘要:
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.
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