A refined method of robust smoothing

Val Gebski, Don McNeil

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Nonlinear data smoothers (filters) have the drawback that too many local peaks and troughs in a data sequence may be preserved. If Gaussian assumptions are not met, linear smoothers do not offer a desirable alternative. A refined method of smoothing out local peaks and troughs, while retaining the broad ones, is proposed. When used for signal recovery from data sequences contaminated with noise, this procedure, termed splicing, appears superior to other methods considered. Application to a real data sequence is presented as an illustration of the technique.

Original languageEnglish
Pages (from-to)616-623
Number of pages8
JournalJournal of the American Statistical Association
Volume79
Issue number387
DOIs
Publication statusPublished - Sep 1984

Keywords

  • linear filters
  • Monte Carlo simulation
  • nonlinear filters
  • robust smoothing
  • running medians of 3
  • signal recovery
  • splicing
  • time series data

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