On local smoothing of nonparametric curve estimators

Jianqing Fan, Peter Hall, Michael A. Martin, Prakash Patil

Research output: Contribution to journalArticle

33 Scopus citations

Abstract

We develop new local versions of familiar smoothing methods, such as cross-validation and smoothed cross-validation, in the contexts of density estimation and regression. These new methods are locally adaptive in the sense that they capture smooth local fluctuations in the curve by using smoothly varying bandwidths that change as the character of the curve changes. Moreover, the new methods are accurate, easy to apply, and computationally expedient.

Original languageEnglish (US)
Pages (from-to)258-266
Number of pages9
JournalJournal of the American Statistical Association
Volume91
Issue number433
DOIs
StatePublished - Mar 1 1996
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Adaptive smoothing
  • Bandwidth
  • Cross-validation
  • Curve estimation
  • Density estimation
  • Kernel methods
  • Local smoothing
  • Nonparametric regression
  • Wavelets

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