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Cited by 5,708

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

Altman, N. S.·1992·The American Statistician
DOI 10.1080/00031305.1992.10475879
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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.

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Citations

Cites

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Metadata

Authors
Altman, N. S.
Venue
The American Statistician
Publisher
Informa UK Limited
Published
1992-08
Volume
46
Issue
3
Pages
175-185
ISSN
  • 0003-1305 (ISSN-L)
  • 1537-2731
DOI
10.1080/00031305.1992.10475879
OpenAlex
W1985258161
Cited by
5,708
References
4
FWCI
4.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. https://app.cochat.ai/papers/10.1080/00031305.1992.10475879
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Altman, N. S. (1992). An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. The American Statistician. https://doi.org/10.1080/00031305.1992.10475879

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