Bootstrap selection of bandwidth and confidence bands for nonparametric regression

Research output: Contribution to journalArticle

Abstract

A bootstrap method is developed to estimate the average squared error of a kernel based nonparametric regression estimator for a given bandwidth. This estimated average squared error is then minimised over the bandwidth to produce a regression estimate. Locally adaptive smoothing and simultaneous confidence bands may be obtained from this bootstrap method.

Original languageEnglish
Pages (from-to)37-44
Number of pages8
JournalJournal of Statistical Computation and Simulation
Volume37
Issue number1-2
DOIs
Publication statusPublished - 1 Oct 1990

Keywords

  • bandwith selection
  • bootstrap
  • confidence bands
  • Nonparametric regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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