Local regression with meaningful parameters

Rafael A. Irizarry

Research output: Contribution to journalArticle

Abstract

Local regression, or loess, has become a popular method for smoothing scatterplots and for nonparametric regression in general. The final result is a "smoothed" version of the data. To obtain the value of the smooth estimate associated with a given covariate a polynomial, usually a line, is fitted locally using weighted least squares. This article presents a version of local regression that fits more general parametric functions. In certain cases, the fitted parameters may be interpreted in some way and we call them meaningful parameters. Examples are included that show how this procedure is useful for signal processing, physiological, and financial data.

Original languageEnglish (US)
Pages (from-to)72-79
Number of pages8
JournalAmerican Statistician
Volume55
Issue number1
DOIs
StatePublished - Feb 2001

Fingerprint

Local Regression
Financial Data
Weighted Least Squares
Nonparametric Regression
Signal Processing
Covariates
Smoothing
Polynomial
Line
Estimate
Weighted least squares
Financial data
Nonparametric regression
Polynomials

Keywords

  • Circadian pattern
  • Harmonic model
  • Local regression
  • Meaningful parameters
  • Sound analysis

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

Cite this

Local regression with meaningful parameters. / Irizarry, Rafael A.

In: American Statistician, Vol. 55, No. 1, 02.2001, p. 72-79.

Research output: Contribution to journalArticle

Irizarry, Rafael A. / Local regression with meaningful parameters. In: American Statistician. 2001 ; Vol. 55, No. 1. pp. 72-79.
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