An introduction to kernel and nearest-neighbor nonparametric regression

N. S. Altman

Research output: Contribution to journalArticlepeer-review

3116 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)175-185
Number of pages11
JournalAmerican Statistician
Issue number3
StatePublished - Aug 1992

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty


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