Stepwise regression is an alternative to splines for fitting noisy data

Thomas J. Burkholder, Richard L. Lieber*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

In this study, we compared numerical methods that are used to fit noisy data. Comparisons included polynominal regression, stepwise polynomial regression and quintic spline approximation. The advantages and limitations of each method are discussed in terms of curve fit quality, computational speed and ease, and solution compactness, Overall, the spline approximation and stepwise polynomial regression provide the best fits to the data. Stepwise regression provides the added utility of providing a simple, unconstrained function which can be easily implemented in simulation studies.

Original languageEnglish (US)
Pages (from-to)235-238
Number of pages4
JournalJournal of Biomechanics
Volume29
Issue number2
DOIs
StatePublished - Feb 1996

ASJC Scopus subject areas

  • Biophysics
  • Orthopedics and Sports Medicine
  • Biomedical Engineering
  • Rehabilitation

Fingerprint Dive into the research topics of 'Stepwise regression is an alternative to splines for fitting noisy data'. Together they form a unique fingerprint.

Cite this