Fast Implementations of Nonparametric Curve Estimators

Jianqing Fan, James S. Marron

Research output: Contribution to journalArticlepeer-review

162 Scopus citations

Abstract

Recent proposals for implementation of kernel-based nonparametric curve estimators are seen to be faster than naive direct implementations by factors up into the hundreds. The main ideas behind the two different approaches are made clear. Careful speed comparisons in a variety of settings and using a variety of machines and software are done. Various issues on computational accuracy and stability are also discussed. Our speed tests show that the fast methods are as fast or somewhat faster than methods traditionally considered very fast, such as LOWESS and smoothing splines.

Original languageEnglish (US)
Pages (from-to)35-56
Number of pages22
JournalJournal of Computational and Graphical Statistics
Volume3
Issue number1
DOIs
StatePublished - Mar 1994
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

Keywords

  • Binning
  • Fast computation
  • Kernel methods
  • Nonparametric curve estimation
  • Smoothing
  • Updating

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